#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();
}
}
}