Index: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj
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--- stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj (revision 10439)
+++ stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj (revision 10441)
@@ -120,5 +120,4 @@
-
Index: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs
===================================================================
--- stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs (revision 10439)
+++ (revision )
@@ -1,86 +1,0 @@
-#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2013 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;
-using System.Collections.Generic;
-using System.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
- [Item("Mean relative error Evaluator", "Evaluator for symbolic regression models that calculates the mean relative error avg( |y' - y| / (|y| + 1))." +
- "The +1 is necessary to handle data with the value of 0.0 correctly. " +
- "Notice: Linear scaling is ignored for this evaluator.")]
- [StorableClass]
- public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
- public override bool Maximization { get { return false; } }
- [StorableConstructor]
- protected SymbolicRegressionMeanRelativeErrorEvaluator(bool deserializing) : base(deserializing) { }
- protected SymbolicRegressionMeanRelativeErrorEvaluator(SymbolicRegressionMeanRelativeErrorEvaluator original, Cloner cloner)
- : base(original, cloner) {
- }
- public override IDeepCloneable Clone(Cloner cloner) {
- return new SymbolicRegressionMeanRelativeErrorEvaluator(this, cloner);
- }
- public SymbolicRegressionMeanRelativeErrorEvaluator() : base() { }
-
- public override IOperation InstrumentedApply() {
- var solution = SymbolicExpressionTreeParameter.ActualValue;
- IEnumerable rows = GenerateRowsToEvaluate();
-
- double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
- QualityParameter.ActualValue = new DoubleValue(quality);
-
- return base.InstrumentedApply();
- }
-
- public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable rows) {
- IEnumerable estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
- IEnumerable targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
- IEnumerable boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-
- var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
-
- OnlineCalculatorError errorState;
- OnlineCalculatorError varErrorState;
- double mre;
- double variance;
- OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
- if (errorState != OnlineCalculatorError.None) return double.NaN;
- return mre;
- }
-
- public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) {
- SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
- EstimationLimitsParameter.ExecutionContext = context;
-
- double mre = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
-
- SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
- EstimationLimitsParameter.ExecutionContext = null;
-
- return mre;
- }
- }
-}