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source: trunk/sources/HeuristicLab.Common/3.3/EnumerableStatisticExtensions.cs @ 3484

Last change on this file since 3484 was 3462, checked in by gkronber, 15 years ago

Refactored symbolic expression tree encoding and problem classes for symbolic regression. #937 , #938

File size: 3.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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.Text;
25using System.Linq;
26
27namespace HeuristicLab.Common {
28  public static class EnumerableStatisticExtensions {
29    /// <summary>
30    /// Calculates the median element of the enumeration.
31    /// </summary>
32    /// <param name="values"></param>
33    /// <returns></returns>
34    public static double Median(this IEnumerable<double> values) {
35      int n = values.Count();
36      if (n == 0) throw new InvalidOperationException("Enumeration contains no elements.");
37
38      double[] sortedValues = new double[n];
39      int i = 0;
40      foreach (double x in values)
41        sortedValues[i++] = x;
42
43      Array.Sort(sortedValues);
44
45      // return the middle element (if n is uneven) or the average of the two middle elements if n is even.
46      if (n % 2 == 1) {
47        return sortedValues[n / 2];
48      } else {
49        return (sortedValues[(n / 2) - 1] + sortedValues[n / 2]) / 2.0;
50      }
51    }
52
53
54    /// <summary>
55    /// Calculates the standard deviation of values.
56    /// </summary>
57    /// <param name="values"></param>
58    /// <returns></returns>
59    public static double StandardDeviation(this IEnumerable<double> values) {
60      return Math.Sqrt(Variance(values));
61    }
62
63    /// <summary>
64    /// Calculates the variance of values. (sum (x - x_mean)² / n)
65    /// </summary>
66    /// <param name="values"></param>
67    /// <returns></returns>
68    public static double Variance(this IEnumerable<double> values) {
69      IList<double> list = values as IList<double>;
70      if (list == null) {
71        list = values.ToList();
72      }
73      if (list.Count == 0) throw new ArgumentException("Enumeration contains no elements.");
74
75      double mean = list.Average();
76      double squaredErrorsSum = 0.0;
77      int n = list.Count;
78      int s = 0;
79      for (int i = 0; i < n; i++) {
80        if (!double.IsNaN(list[i])) {
81          double d = list[i] - mean;
82          squaredErrorsSum += d * d;
83          s++;
84        }
85      }
86      if (s == 0) {
87        throw new ArgumentException("Enumeration contains no non-NaN elements.");
88      }
89      return squaredErrorsSum / n;
90    }
91  }
92}
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