1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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 System.Collections.Generic;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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25 |
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26 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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27 | public abstract class SymbolicDataAnalysisSolutionValuesCalculator {
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28 | protected readonly ISymbolicExpressionTree tempTree;
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29 | protected readonly ConstantTreeNode constantNode;
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30 |
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31 | public SymbolicDataAnalysisSolutionValuesCalculator() {
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32 | constantNode = ((ConstantTreeNode)new Constant().CreateTreeNode());
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33 | ISymbolicExpressionTreeNode root = new ProgramRootSymbol().CreateTreeNode();
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34 | ISymbolicExpressionTreeNode start = new StartSymbol().CreateTreeNode();
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35 | root.AddSubtree(start);
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36 | tempTree = new SymbolicExpressionTree(root);
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37 | }
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38 |
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39 | // should be moved to an interface, then un-abstract the class
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40 | public abstract Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData);
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41 | public abstract Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, double lowerEstimationLimit, double upperEstimationLimit);
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42 |
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43 | protected void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
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44 | for (int i = 0; i < root.SubtreeCount; i++) {
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45 | if (root.GetSubtree(i) == oldBranch) {
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46 | root.RemoveSubtree(i);
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47 | root.InsertSubtree(i, newBranch);
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48 | return;
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49 | }
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50 | }
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51 | }
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52 |
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53 | protected double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData) {
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54 | // remove old ADFs
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55 | while (tempTree.Root.SubtreeCount > 1) tempTree.Root.RemoveSubtree(1);
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56 | // clone ADFs of source tree
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57 | for (int i = 1; i < sourceTree.Root.SubtreeCount; i++) {
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58 | tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
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59 | }
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60 | var start = tempTree.Root.GetSubtree(0);
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61 | while (start.SubtreeCount > 0) start.RemoveSubtree(0);
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62 | start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
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63 | var rows = problemData.TrainingIndices;
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64 | return interpreter.GetSymbolicExpressionTreeValues(tempTree, problemData.Dataset, rows).Median();
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65 | }
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66 | }
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67 | }
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