1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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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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19 | */
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20 | #endregion
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21 |
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22 | using HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Data;
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25 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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26 | using HeuristicLab.Operators;
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27 | using HeuristicLab.Parameters;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
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32 | /// <summary>
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33 | /// An operator that creates a linearly transformed symbolic regression solution (given alpha and beta).
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34 | /// </summary>
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35 | [Item("SymbolicRegressionSolutionLinearScaler", "An operator that creates a linearly transformed symbolic regression solution (given alpha and beta).")]
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36 | [StorableClass]
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37 | public sealed class SymbolicRegressionSolutionLinearScaler : SingleSuccessorOperator {
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38 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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39 | private const string ScaledSymbolicExpressionTreeParameterName = "ScaledSymbolicExpressionTree";
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40 | private const string AlphaParameterName = "Alpha";
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41 | private const string BetaParameterName = "Beta";
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42 |
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43 | public ILookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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44 | get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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45 | }
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46 | public ILookupParameter<SymbolicExpressionTree> ScaledSymbolicExpressionTreeParameter {
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47 | get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[ScaledSymbolicExpressionTreeParameterName]; }
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48 | }
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49 | public ILookupParameter<DoubleValue> AlphaParameter {
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50 | get { return (ILookupParameter<DoubleValue>)Parameters[AlphaParameterName]; }
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51 | }
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52 | public ILookupParameter<DoubleValue> BetaParameter {
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53 | get { return (ILookupParameter<DoubleValue>)Parameters[BetaParameterName]; }
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54 | }
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55 |
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56 | [StorableConstructor]
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57 | private SymbolicRegressionSolutionLinearScaler(bool deserializing) : base(deserializing) { }
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58 | private SymbolicRegressionSolutionLinearScaler(SymbolicRegressionSolutionLinearScaler original, Cloner cloner) : base(original, cloner) { }
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59 | public SymbolicRegressionSolutionLinearScaler()
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60 | : base() {
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61 | Parameters.Add(new LookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to transform."));
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62 | Parameters.Add(new LookupParameter<SymbolicExpressionTree>(ScaledSymbolicExpressionTreeParameterName, "The resulting symbolic expression trees after transformation."));
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63 | Parameters.Add(new LookupParameter<DoubleValue>(AlphaParameterName, "Alpha parameter for linear transformation."));
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64 | Parameters.Add(new LookupParameter<DoubleValue>(BetaParameterName, "Beta parameter for linear transformation."));
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65 | }
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66 |
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67 | public override IDeepCloneable Clone(Cloner cloner) {
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68 | return new SymbolicRegressionSolutionLinearScaler(this, cloner);
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69 | }
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70 |
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71 | public override IOperation Apply(IExecutionContext context) {
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72 | SymbolicExpressionTree tree = SymbolicExpressionTreeParameter.ActualValue;
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73 | DoubleValue alpha = AlphaParameter.ActualValue;
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74 | DoubleValue beta = BetaParameter.ActualValue;
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75 | if (alpha != null && beta != null) {
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76 | ScaledSymbolicExpressionTreeParameter.ActualValue = Scale(tree, alpha.Value, beta.Value);
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77 | } else {
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78 | // alpha or beta parameter not available => do not scale tree
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79 | ScaledSymbolicExpressionTreeParameter.ActualValue = tree;
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80 | }
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81 |
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82 | return base.Apply(context);
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83 | }
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84 |
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85 | public static SymbolicExpressionTree Scale(SymbolicExpressionTree original, double alpha, double beta) {
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86 | var mainBranch = original.Root.SubTrees[0].SubTrees[0];
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87 | var scaledMainBranch = MakeSum(MakeProduct(beta, mainBranch), alpha);
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88 |
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89 | // remove the main branch before cloning to prevent cloning of sub-trees
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90 | original.Root.SubTrees[0].RemoveSubTree(0);
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91 | var scaledTree = (SymbolicExpressionTree)original.Clone();
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92 | // insert main branch into the original tree again
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93 | original.Root.SubTrees[0].InsertSubTree(0, mainBranch);
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94 | // insert the scaled main branch into the cloned tree
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95 | scaledTree.Root.SubTrees[0].InsertSubTree(0, scaledMainBranch);
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96 | return scaledTree;
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97 | }
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98 |
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99 | private static SymbolicExpressionTreeNode MakeSum(SymbolicExpressionTreeNode treeNode, double alpha) {
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100 | var node = (new Addition()).CreateTreeNode();
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101 | var alphaConst = MakeConstant(alpha);
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102 | node.AddSubTree(treeNode);
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103 | node.AddSubTree(alphaConst);
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104 | return node;
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105 | }
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106 |
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107 | private static SymbolicExpressionTreeNode MakeProduct(double beta, SymbolicExpressionTreeNode treeNode) {
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108 | var node = (new Multiplication()).CreateTreeNode();
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109 | var betaConst = MakeConstant(beta);
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110 | node.AddSubTree(treeNode);
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111 | node.AddSubTree(betaConst);
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112 | return node;
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113 | }
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114 |
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115 | private static SymbolicExpressionTreeNode MakeConstant(double c) {
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116 | var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
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117 | node.Value = c;
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118 | return node;
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119 | }
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120 | }
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121 | }
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