[369] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2008 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;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using System.Text;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Functions;
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| 30 | using HeuristicLab.DataAnalysis;
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| 31 |
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| 32 | namespace HeuristicLab.StructureIdentification {
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| 33 | public class TheilInequalityCoefficientEvaluator : GPEvaluatorBase {
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| 34 | public override string Description {
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| 35 | get {
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| 36 | return @"Evaluates 'FunctionTree' for all samples of 'Dataset' and calculates
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| 37 | the 'Theil inequality coefficient (scale invariant)' of estimated values vs. real values of 'TargetVariable'.";
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| 38 | }
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| 39 | }
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| 40 |
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| 41 | public TheilInequalityCoefficientEvaluator()
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| 42 | : base() {
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| 43 | }
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| 44 |
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| 45 | public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
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| 46 | int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
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| 47 | int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
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| 48 | double errorsSquaredSum = 0.0;
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| 49 | double estimatedSquaredSum = 0.0;
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| 50 | double originalSquaredSum = 0.0;
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| 51 | functionTree.PrepareEvaluation(dataset);
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| 52 | for(int sample = trainingStart; sample < trainingEnd; sample++) {
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| 53 | double prevValue = dataset.GetValue(sample - 1, targetVariable);
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| 54 | double estimatedChange = functionTree.Evaluate(sample) - prevValue;
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| 55 | double originalChange = dataset.GetValue(sample, targetVariable) - prevValue;
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| 56 | if(!double.IsNaN(originalChange) && !double.IsInfinity(originalChange)) {
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| 57 | if(double.IsNaN(estimatedChange) || double.IsInfinity(estimatedChange))
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| 58 | estimatedChange = maximumPunishment;
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| 59 | else if(estimatedChange > maximumPunishment)
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| 60 | estimatedChange = maximumPunishment;
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| 61 | else if(estimatedChange < -maximumPunishment)
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| 62 | estimatedChange = - maximumPunishment;
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| 63 |
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| 64 | double error = estimatedChange - originalChange;
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| 65 | errorsSquaredSum += error * error;
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| 66 | estimatedSquaredSum += estimatedChange * estimatedChange;
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| 67 | originalSquaredSum += originalChange * originalChange;
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| 68 | }
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| 69 | }
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| 70 | int nSamples = trainingEnd - trainingStart;
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| 71 | double quality = Math.Sqrt(errorsSquaredSum / nSamples) / (Math.Sqrt(estimatedSquaredSum/nSamples) + Math.Sqrt(originalSquaredSum/nSamples));
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| 72 | if(double.IsNaN(quality) || double.IsInfinity(quality))
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| 73 | quality = double.MaxValue;
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| 74 | return quality;
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| 75 | }
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| 76 | }
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| 77 | }
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