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source: branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/EnumerableStatisticsExtension.cs @ 10189

Last change on this file since 10189 was 10017, checked in by ascheibe, 11 years ago

#2031 improved sample size estimation and some minor improvements

File size: 2.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26
27namespace HeuristicLab.Analysis.Statistics {
28  //TODO: This should be moved to HeuristicLab.Common on trunk integration
29  public static class EnumerableStatisticExtensions {
30    public static Tuple<double, double> ConfidenceIntervals(this IEnumerable<double> values, double alpha) {
31      if (values.Count() <= 1) return new Tuple<double, double>(double.NaN, double.NaN);
32
33      double lower, upper;
34      double s = values.StandardDeviation();
35      double x = values.Average();
36      int n = values.Count();
37      double t = alglib.studenttdistribution(n - 1, 1 - (alpha / 2));
38
39      lower = x - t * (s / Math.Sqrt(n));
40      upper = x + t * (s / Math.Sqrt(n));
41
42      return new Tuple<double, double>(lower, upper);
43    }
44
45    // Bessel corrected variance
46    public static double EstimatedVariance(this IEnumerable<double> values) {
47      double n = values.Count();
48      return values.Variance() * n / (n - 1);
49    }
50
51    // Bessel corrected standard deviation
52    public static double EstimatedStandardDeviation(this IEnumerable<double> values) {
53      double n = values.Count();
54      return values.StandardDeviation() * n / (n - 1);
55    }
56  }
57}
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