# source:branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/LinearLeastSquaresFitting.cs@9353

Last change on this file since 9353 was 9353, checked in by ascheibe, 6 years ago

#2031 initial import of views for statistical testing

File size: 1.9 KB
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2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Linq;
24
25namespace HeuristicLab.Analysis.Statistics {
26  public class LinearLeastSquaresFitting {
27    public static void Calculate(double[] dataPoints, out double a1, out double a0) {
28      double sxy = 0.0;
29      double sxx = 0.0;
30      int n = dataPoints.Count();
31      double sy = dataPoints.Sum();
32      double sx = ((n - 1) * n) / 2;
33      double avgy = sy / n;
34      double avgx = sx / n;
35
36      for (int i = 0; i < n; i++) {
37        sxy += i * dataPoints[i];
38        sxx += i * i;
39      }
40
41      a1 = (sxy - (n * avgx * avgy)) / (sxx - (n * avgx * avgx));
42      a0 = avgy - a1 * avgx;
43    }
44
45    public static double CalculateError(double[] dataPoints, double a1, double a0) {
46      double r = 0.0;
47      double avgy = dataPoints.Average();
48      double sstot = 0.0;
49      double sserr = 0.0;
50
51      for (int i = 0; i < dataPoints.Count(); i++) {
52        double y = a1 * i + a0;
53        sstot += Math.Pow(dataPoints[i] - avgy, 2);
54        sserr += Math.Pow(dataPoints[i] - y, 2);
55      }
56
57      r = 1.0 - (sserr / sstot);
58      return r;
59    }
60  }
61}
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