#region License Information /* HeuristicLab * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; namespace HeuristicLab.Problems.DataAnalysis.Symbolic { public abstract class SymbolicDataAnalysisSolutionImpactValuesCalculator { // should be moved to an interface, then un-abstract the class public abstract Dictionary CalculateReplacementValues(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData); public abstract Dictionary CalculateImpactValues(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, double lowerEstimationLimit, double upperEstimationLimit); protected void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) { for (int i = 0; i < root.SubtreeCount; i++) { if (root.GetSubtree(i) == oldBranch) { root.RemoveSubtree(i); root.InsertSubtree(i, newBranch); return; } } } protected double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData) { var root = new ProgramRootSymbol().CreateTreeNode(); var start = new StartSymbol().CreateTreeNode(); root.AddSubtree(start); start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone()); var rows = problemData.TrainingIndices; var tempTree = new SymbolicExpressionTree(root); return interpreter.GetSymbolicExpressionTreeValues(tempTree, problemData.Dataset, rows).Median(); } } }