[645] | 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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[702] | 29 | using HeuristicLab.DataAnalysis;
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[645] | 30 |
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| 31 | namespace HeuristicLab.GP.StructureIdentification {
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| 32 | public class MeanSquaredErrorEvaluator : GPEvaluatorBase {
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| 33 | public override string Description {
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| 34 | get {
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| 35 | return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates the mean-squared-error
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| 36 | for the estimated values vs. the real values of 'TargetVariable'.";
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| 37 | }
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| 38 | }
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| 39 |
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| 40 | public MeanSquaredErrorEvaluator()
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| 41 | : base() {
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| 42 | AddVariableInfo(new VariableInfo("MSE", "The mean squared error of the model", typeof(DoubleData), VariableKind.New));
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| 43 | }
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| 44 |
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[1796] | 45 | public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
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[645] | 46 | double errorsSquaredSum = 0;
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[1794] | 47 | int n = 0;
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[712] | 48 | for (int sample = start; sample < end; sample++) {
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| 49 | double original = dataset.GetValue(sample, targetVariable);
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[1796] | 50 | double estimated = evaluator.Evaluate(tree, sample);
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[712] | 51 | if (updateTargetValues) {
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| 52 | dataset.SetValue(sample, targetVariable, estimated);
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[702] | 53 | }
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[712] | 54 | if (!double.IsNaN(original) && !double.IsInfinity(original)) {
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[645] | 55 | double error = estimated - original;
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| 56 | errorsSquaredSum += error * error;
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[1794] | 57 | n++;
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[645] | 58 | }
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| 59 | }
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| 60 |
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[1794] | 61 | errorsSquaredSum /= n;
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[712] | 62 | if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
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[645] | 63 | errorsSquaredSum = double.MaxValue;
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| 64 | }
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[702] | 65 |
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| 66 | DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
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[712] | 67 | if (mse == null) {
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[702] | 68 | mse = new DoubleData();
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| 69 | scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
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| 70 | }
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| 71 |
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[645] | 72 | mse.Data = errorsSquaredSum;
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| 73 | }
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| 74 | }
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| 75 | }
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