[9998] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2013 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 HeuristicLab.Common;
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| 26 |
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| 27 | namespace HeuristicLab.Analysis.Statistics {
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| 28 | //TODO: This should be moved to HeuristicLab.Common on trunk integration
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| 29 | public static class EnumerableStatisticExtensions {
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| 30 | public static Tuple<double, double> ConfidenceIntervals(this IEnumerable<double> values, double alpha) {
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[10017] | 31 | if (values.Count() <= 1) return new Tuple<double, double>(double.NaN, double.NaN);
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| 32 |
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[9998] | 33 | double lower, upper;
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| 34 | double s = values.StandardDeviation();
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| 35 | double x = values.Average();
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| 36 | int n = values.Count();
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| 37 | double t = alglib.studenttdistribution(n - 1, 1 - (alpha / 2));
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| 38 |
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| 39 | lower = x - t * (s / Math.Sqrt(n));
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| 40 | upper = x + t * (s / Math.Sqrt(n));
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| 41 |
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| 42 | return new Tuple<double, double>(lower, upper);
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| 43 | }
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[10017] | 44 |
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| 45 | // Bessel corrected variance
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| 46 | public static double EstimatedVariance(this IEnumerable<double> values) {
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| 47 | double n = values.Count();
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| 48 | return values.Variance() * n / (n - 1);
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| 49 | }
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| 50 |
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| 51 | // Bessel corrected standard deviation
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| 52 | public static double EstimatedStandardDeviation(this IEnumerable<double> values) {
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| 53 | double n = values.Count();
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| 54 | return values.StandardDeviation() * n / (n - 1);
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| 55 | }
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[9998] | 56 | }
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| 57 | }
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