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source: trunk/sources/HeuristicLab.Problems.GrammaticalEvolution/3.4/SymbolicRegression/GESymbolicExpressionGrammar.cs @ 14093

Last change on this file since 14093 was 12012, checked in by ascheibe, 10 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 6.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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
23#endregion
24
25using System.Collections.Generic;
26using System.Linq;
27using HeuristicLab.Common;
28using HeuristicLab.Core;
29using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31using HeuristicLab.Problems.DataAnalysis.Symbolic;
32using HeuristicLab.Random;
33
34namespace HeuristicLab.Problems.GrammaticalEvolution {
35  [StorableClass]
36  [Item("GESymbolicExpressionGrammar", "Represents a grammar for functional expressions for grammatical evolution.")]
37  public class GESymbolicExpressionGrammar : SymbolicExpressionGrammar, ISymbolicDataAnalysisGrammar {
38    [StorableConstructor]
39    protected GESymbolicExpressionGrammar(bool deserializing) : base(deserializing) { }
40    protected GESymbolicExpressionGrammar(GESymbolicExpressionGrammar original, Cloner cloner) : base(original, cloner) { }
41    public GESymbolicExpressionGrammar()
42      : base(ItemAttribute.GetName(typeof(GESymbolicExpressionGrammar)), ItemAttribute.GetDescription(typeof(GESymbolicExpressionGrammar))) {
43      // empty ctor is necessary to allow creation of new GEGrammars from the GUI.
44      // the problem creates a new correctly configured grammar when the grammar is set
45    }
46    internal GESymbolicExpressionGrammar(IEnumerable<string> variableNames, int nConstants)
47      : base(ItemAttribute.GetName(typeof(GESymbolicExpressionGrammar)), ItemAttribute.GetDescription(typeof(GESymbolicExpressionGrammar))) {
48      // this ctor is called by the problem as only the problem knows the allowed input variables
49      Initialize(variableNames, nConstants);
50    }
51    public override IDeepCloneable Clone(Cloner cloner) {
52      return new GESymbolicExpressionGrammar(this, cloner);
53    }
54
55    private void Initialize(IEnumerable<string> variableNames, int nConstants) {
56      #region symbol declaration
57      var add = new Addition();
58      var sub = new Subtraction();
59      var mul = new Multiplication();
60      var div = new Division();
61      var mean = new Average();
62      var log = new Logarithm();
63      var pow = new Power();
64      var square = new Square();
65      var root = new Root();
66      var sqrt = new SquareRoot();
67      var exp = new Exponential();
68
69      // we use our own random number generator here because we assume
70      // that grammars are only initialized once when setting the grammar in the problem.
71      // This means everytime the grammar parameter in the problem is changed
72      // we initialize the constants to new values
73      var rand = new MersenneTwister();
74      // warm up
75      for (int i = 0; i < 1000; i++) rand.NextDouble();
76
77      var constants = new List<Constant>(nConstants);
78      for (int i = 0; i < nConstants; i++) {
79        var constant = new Constant();
80        do {
81          var constVal = rand.NextDouble() * 20.0 - 10.0;
82          constant.Name = string.Format("{0:0.000}", constVal);
83          constant.MinValue = constVal;
84          constant.MaxValue = constVal;
85          constant.ManipulatorSigma = 0.0;
86          constant.ManipulatorMu = 0.0;
87          constant.MultiplicativeManipulatorSigma = 0.0;
88        } while (constants.Any(c => c.Name == constant.Name)); // unlikely, but it could happen that the same constant value is sampled twice. so we resample if necessary.
89        constants.Add(constant);
90      }
91
92      var variables = new List<HeuristicLab.Problems.DataAnalysis.Symbolic.Variable>();
93      foreach (var variableName in variableNames) {
94        var variableSymbol = new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable();
95        variableSymbol.Name = variableName;
96        variableSymbol.WeightManipulatorMu = 0.0;
97        variableSymbol.WeightManipulatorSigma = 0.0;
98        variableSymbol.WeightMu = 1.0;
99        variableSymbol.WeightSigma = 0.0;
100        variableSymbol.MultiplicativeWeightManipulatorSigma = 0.0;
101        variableSymbol.AllVariableNames = new[] { variableName };
102        variableSymbol.VariableNames = new[] { variableName };
103        variables.Add(variableSymbol);
104      }
105
106      #endregion
107
108      AddSymbol(add);
109      AddSymbol(sub);
110      AddSymbol(mul);
111      AddSymbol(div);
112      AddSymbol(mean);
113      AddSymbol(log);
114      AddSymbol(pow);
115      AddSymbol(square);
116      AddSymbol(root);
117      AddSymbol(sqrt);
118      AddSymbol(exp);
119      constants.ForEach(AddSymbol);
120      variables.ForEach(AddSymbol);
121
122      #region subtree count configuration
123      SetSubtreeCount(add, 2, 2);
124      SetSubtreeCount(sub, 2, 2);
125      SetSubtreeCount(mul, 2, 2);
126      SetSubtreeCount(div, 2, 2);
127      SetSubtreeCount(mean, 2, 2);
128      SetSubtreeCount(log, 1, 1);
129      SetSubtreeCount(pow, 2, 2);
130      SetSubtreeCount(square, 1, 1);
131      SetSubtreeCount(root, 2, 2);
132      SetSubtreeCount(sqrt, 1, 1);
133      SetSubtreeCount(exp, 1, 1);
134      constants.ForEach((c) => SetSubtreeCount(c, 0, 0));
135      variables.ForEach((v) => SetSubtreeCount(v, 0, 0));
136      #endregion
137
138      var functions = new ISymbol[] { add, sub, mul, div, mean, log, pow, root, square, sqrt };
139      var terminalSymbols = variables.Concat<ISymbol>(constants);
140      var allSymbols = functions.Concat(terminalSymbols);
141
142      #region allowed child symbols configuration
143      foreach (var s in allSymbols) {
144        AddAllowedChildSymbol(StartSymbol, s);
145      }
146      foreach (var parentSymb in functions)
147        foreach (var childSymb in allSymbols) {
148          AddAllowedChildSymbol(parentSymb, childSymb);
149        }
150
151      #endregion
152    }
153  }
154}
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