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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/SymbolicVectorRegressionSolutionLinearScaler.cs @ 12711

Last change on this file since 12711 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 6.2 KB
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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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Operators;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
31using HeuristicLab.Common;
32
33namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic {
34  /// <summary>
35  /// An operator that creates a linearly transformed symbolic regression solution (given alpha and beta).
36  /// </summary>
37  [Item("SymbolicVectorRegressionSolutionLinearScaler", "An operator that creates a linearly transformed symbolic regression solution (given alpha and beta).")]
38  [StorableClass]
39  public sealed class SymbolicVectorRegressionSolutionLinearScaler : SingleSuccessorOperator {
40    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
41    private const string ScaledSymbolicExpressionTreeParameterName = "ScaledSymbolicExpressionTree";
42    private const string AlphaParameterName = "Alpha";
43    private const string BetaParameterName = "Beta";
44
45    public ILookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
46      get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
47    }
48    public ILookupParameter<SymbolicExpressionTree> ScaledSymbolicExpressionTreeParameter {
49      get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[ScaledSymbolicExpressionTreeParameterName]; }
50    }
51    public ILookupParameter<DoubleArray> AlphaParameter {
52      get { return (ILookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
53    }
54    public ILookupParameter<DoubleArray> BetaParameter {
55      get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
56    }
57    [StorableConstructor]
58    protected SymbolicVectorRegressionSolutionLinearScaler(bool deserializing) : base(deserializing) { }
59    protected SymbolicVectorRegressionSolutionLinearScaler(SymbolicVectorRegressionSolutionLinearScaler original, Cloner cloner)
60      : base(original, cloner) {
61    }
62    public SymbolicVectorRegressionSolutionLinearScaler()
63      : base() {
64      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to transform."));
65      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(ScaledSymbolicExpressionTreeParameterName, "The resulting symbolic expression trees after transformation."));
66      Parameters.Add(new LookupParameter<DoubleArray>(AlphaParameterName, "Alpha parameter for linear transformation."));
67      Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "Beta parameter for linear transformation."));
68    }
69    public override IDeepCloneable Clone(Cloner cloner) {
70      return new SymbolicVectorRegressionSolutionLinearScaler(this, cloner);
71    }
72    public override IOperation Apply() {
73      SymbolicExpressionTree tree = SymbolicExpressionTreeParameter.ActualValue;
74      DoubleArray alpha = AlphaParameter.ActualValue;
75      DoubleArray beta = BetaParameter.ActualValue;
76      if (alpha != null && beta != null) {
77        ScaledSymbolicExpressionTreeParameter.ActualValue = Scale(tree, beta.ToArray(), alpha.ToArray());
78      } else {
79        // alpha or beta parameter not available => do not scale tree
80        ScaledSymbolicExpressionTreeParameter.ActualValue = tree;
81      }
82
83      return base.Apply();
84    }
85
86    public static SymbolicExpressionTree Scale(SymbolicExpressionTree original, double[] beta, double[] alpha) {
87      List<SymbolicExpressionTreeNode> resultProducingBranches = new List<SymbolicExpressionTreeNode>(original.Root.SubTrees[0].SubTrees);
88      // remove the main branch before cloning to prevent cloning of sub-trees
89      while (original.Root.SubTrees[0].SubTrees.Count > 0)
90        original.Root.SubTrees[0].RemoveSubTree(0);
91      var scaledTree = (SymbolicExpressionTree)original.Clone();
92      int i = 0;
93      foreach (var resultProducingBranch in resultProducingBranches) {
94        var scaledMainBranch = MakeSum(MakeProduct(beta[i], resultProducingBranch), alpha[i]);
95
96        // insert main branch into the original tree again
97        original.Root.SubTrees[0].AddSubTree(resultProducingBranch);
98        // insert the scaled main branch into the cloned tree
99        scaledTree.Root.SubTrees[0].AddSubTree(scaledMainBranch);
100        i++;
101      }
102      return scaledTree;
103    }
104
105    private static SymbolicExpressionTreeNode MakeSum(SymbolicExpressionTreeNode treeNode, double alpha) {
106      var node = (new Addition()).CreateTreeNode();
107      var alphaConst = MakeConstant(alpha);
108      node.AddSubTree(treeNode);
109      node.AddSubTree(alphaConst);
110      return node;
111    }
112
113    private static SymbolicExpressionTreeNode MakeProduct(double beta, SymbolicExpressionTreeNode treeNode) {
114      var node = (new Multiplication()).CreateTreeNode();
115      var betaConst = MakeConstant(beta);
116      node.AddSubTree(treeNode);
117      node.AddSubTree(betaConst);
118      return node;
119    }
120
121    private static SymbolicExpressionTreeNode MakeConstant(double c) {
122      var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
123      node.Value = c;
124      return node;
125    }
126  }
127}
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