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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionEvaluator.cs @ 4068

Last change on this file since 4068 was 4068, checked in by swagner, 13 years ago

Sorted usings and removed unused usings in entire solution (#1094)

File size: 8.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Operators;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31using HeuristicLab.Problems.DataAnalysis.Regression;
32using HeuristicLab.Problems.DataAnalysis.Symbolic;
33
34namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
35  [StorableClass]
36  public abstract class SymbolicVectorRegressionEvaluator : SingleSuccessorOperator, IMultiVariateDataAnalysisEvaluator {
37    private const string RandomParameterName = "Random";
38    private const string MultiVariateDataAnalysisProblemDataParameterName = "MultiVariateDataAnalysisProblemData";
39    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
40    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
41    private const string SamplesStartParameterName = "SamplesStart";
42    private const string SamplesEndParameterName = "SamplesEnd";
43    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
44    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
45    private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
46
47    #region parameter properties
48    public ILookupParameter<IRandom> RandomParameter {
49      get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
50    }
51    public ILookupParameter<MultiVariateDataAnalysisProblemData> MultiVariateDataAnalysisProblemDataParameter {
52      get { return (ILookupParameter<MultiVariateDataAnalysisProblemData>)Parameters[MultiVariateDataAnalysisProblemDataParameterName]; }
53    }
54    public IValueLookupParameter<IntValue> SamplesStartParameter {
55      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesStartParameterName]; }
56    }
57    public IValueLookupParameter<IntValue> SamplesEndParameter {
58      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesEndParameterName]; }
59    }
60    public IValueLookupParameter<DoubleArray> LowerEstimationLimitParameter {
61      get { return (IValueLookupParameter<DoubleArray>)Parameters[LowerEstimationLimitParameterName]; }
62    }
63    public IValueLookupParameter<DoubleArray> UpperEstimationLimitParameter {
64      get { return (IValueLookupParameter<DoubleArray>)Parameters[UpperEstimationLimitParameterName]; }
65    }
66    public ILookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
67      get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
68    }
69    public ILookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
70      get { return (ILookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
71    }
72    public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
73      get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
74    }
75
76    #endregion
77    #region properties
78    public IRandom Random {
79      get { return RandomParameter.ActualValue; }
80    }
81    public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
82      get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
83    }
84    public SymbolicExpressionTree SymbolicExpressionTree {
85      get { return SymbolicExpressionTreeParameter.ActualValue; }
86    }
87    public MultiVariateDataAnalysisProblemData MultiVariateDataAnalysisProblemData {
88      get { return MultiVariateDataAnalysisProblemDataParameter.ActualValue; }
89    }
90    public IntValue SamplesStart {
91      get { return SamplesStartParameter.ActualValue; }
92    }
93    public IntValue SamplesEnd {
94      get { return SamplesEndParameter.ActualValue; }
95    }
96    public DoubleArray LowerEstimationLimit {
97      get { return LowerEstimationLimitParameter.ActualValue; }
98    }
99    public DoubleArray UpperEstimationLimit {
100      get { return UpperEstimationLimitParameter.ActualValue; }
101    }
102    public PercentValue RelativeNumberOfEvaluatedSamples {
103      get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
104    }
105    #endregion
106
107    public SymbolicVectorRegressionEvaluator()
108      : base() {
109      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "A random number generator."));
110      Parameters.Add(new LookupParameter<MultiVariateDataAnalysisProblemData>(MultiVariateDataAnalysisProblemDataParameterName, "The multi-variate data analysis problem data to use for training."));
111      Parameters.Add(new LookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The tree interpreter that should be used to evaluate the symbolic expression tree."));
112      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first index of the data set partition to use for training."));
113      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The last index of the data set partition to use for training."));
114      Parameters.Add(new ValueLookupParameter<DoubleArray>(UpperEstimationLimitParameterName, "The upper limit for the estimated values for each component."));
115      Parameters.Add(new ValueLookupParameter<DoubleArray>(LowerEstimationLimitParameterName, "The lower limit for the estimated values for each component."));
116      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic vector regression solution encoded as a symbolic expression tree."));
117      Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
118    }
119
120    public override IOperation Apply() {
121      var interpreter = SymbolicExpressionTreeInterpreter;
122      var tree = SymbolicExpressionTree;
123      var problemData = MultiVariateDataAnalysisProblemData;
124
125      IEnumerable<string> selectedTargetVariables =
126        problemData.TargetVariables.CheckedItems
127        .Select(x => x.Value.Value);
128
129      // check if there is a vector component for each target variable
130      if (selectedTargetVariables.Count() != tree.Root.SubTrees[0].SubTrees.Count)
131        throw new ArgumentException("The dimension of the output-vector of the tree doesn't match the number of selected target variables.");
132      int start = SamplesStart.Value;
133      int end = SamplesEnd.Value;
134
135      IEnumerable<int> rows = GenerateRowsToEvaluate((uint)Random.Next(), RelativeNumberOfEvaluatedSamples.Value, start, end);
136
137      Evaluate(tree, interpreter, problemData, selectedTargetVariables, rows, LowerEstimationLimit, UpperEstimationLimit);
138
139      return base.Apply();
140    }
141
142    public abstract void Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound);
143
144    private static IEnumerable<int> GenerateRowsToEvaluate(uint seed, double relativeAmount, int start, int end) {
145      if (end < start) throw new ArgumentException("Start value is larger than end value.");
146      int count = (int)((end - start) * relativeAmount);
147      if (count == 0) count = 1;
148      return RandomEnumerable.SampleRandomNumbers(seed, start, end, count);
149    }
150  }
151}
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