[1888] | 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.DataAnalysis;
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| 29 |
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| 30 | namespace HeuristicLab.Modeling {
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| 31 | /// <summary>
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| 32 | /// The Variance Accounted For (VAF) function calculates is computed as
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| 33 | /// VAF(y,y') = ( 1 - var(y-y')/var(y) )
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| 34 | /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
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| 35 | /// </summary>
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| 36 | public class SimpleVarianceAccountedForEvaluator : SimpleEvaluatorBase {
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| 37 |
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| 38 | public override string OutputVariableName {
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| 39 | get {
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| 40 | return "VAF";
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| 41 | }
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| 42 | }
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| 43 |
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| 44 | public override double Evaluate(double[,] values) {
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[2136] | 45 | try {
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| 46 | return Calculate(values);
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| 47 | }
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| 48 | catch (ArgumentException) {
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| 49 | return double.NegativeInfinity;
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| 50 | }
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[1888] | 51 | }
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| 52 |
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| 53 | public static double Calculate(double[,] values) {
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| 54 | int n = values.GetLength(0);
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| 55 | double[] errors = new double[n];
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| 56 | double[] originalTargetVariableValues = new double[n];
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| 57 | for (int i = 0; i < n; i++) {
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| 58 | double estimated = values[i, 0];
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| 59 | double original = values[i, 1];
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| 60 | if (!double.IsNaN(estimated) && !double.IsInfinity(estimated) &&
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| 61 | !double.IsNaN(original) && !double.IsInfinity(original)) {
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| 62 | errors[i] = original - estimated;
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| 63 | originalTargetVariableValues[i] = original;
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| 64 | } else {
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| 65 | errors[i] = double.NaN;
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| 66 | originalTargetVariableValues[i] = double.NaN;
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| 67 | }
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| 68 | }
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| 69 | double errorsVariance = Statistics.Variance(errors);
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| 70 | double originalsVariance = Statistics.Variance(originalTargetVariableValues);
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[2136] | 71 | if (IsAlmost(originalsVariance, 0.0))
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| 72 | if (IsAlmost(errorsVariance, 0.0)) {
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| 73 | return 1.0;
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| 74 | } else {
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| 75 | throw new ArgumentException("Variance of original values is zero");
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| 76 | } else {
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| 77 | return 1.0 - errorsVariance / originalsVariance;
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| 78 | }
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[1888] | 79 | }
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| 80 | }
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| 81 | }
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