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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/OnlineLinearScalingCalculator.cs @ 12395

Last change on this file since 12395 was 4555, checked in by gkronber, 14 years ago

Improved time series evaluators. #1142

File size: 2.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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 HeuristicLab.Common;
24
25namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
26  /// <summary>
27  /// Calculates linear scaling parameters in one pass.
28  /// The formulas to calculate the scaling parameters were taken from Scaled Symblic Regression by Maarten Keijzer.
29  /// http://www.springerlink.com/content/x035121165125175/
30  /// </summary>
31  public class OnlineLinearScalingCalculator {
32    private int n;
33    private OnlineMeanAndVarianceCalculator yVarianceCalculator;
34    private OnlineMeanAndVarianceCalculator tMeanCalculator;
35    private OnlineCovarianceEvaluator ytCovarianceEvaluator;
36
37    public double Alpha {
38      get {
39        if (n < 2) {
40          return 0.0;
41        } else {
42          return tMeanCalculator.Mean - Beta * yVarianceCalculator.Mean;
43        }
44      }
45    }
46
47    public double Beta {
48      get {
49        if (n < 2 || yVarianceCalculator.PopulationVariance.IsAlmost(0.0)) {
50          return 1;
51        } else {
52          return ytCovarianceEvaluator.Covariance / yVarianceCalculator.PopulationVariance;
53        }
54      }
55    }
56
57    public OnlineLinearScalingCalculator() {
58      Reset();
59    }
60
61    public void Reset() {
62      n = 0;
63      yVarianceCalculator = new OnlineMeanAndVarianceCalculator();
64      tMeanCalculator = new OnlineMeanAndVarianceCalculator();
65      ytCovarianceEvaluator = new OnlineCovarianceEvaluator();
66
67    }
68
69    public void Add(double target, double x) {
70      if (double.IsNaN(target) || double.IsInfinity(target) || double.IsNaN(x) || double.IsInfinity(x))
71        throw new ArgumentException("Linear scaling is not defined for series containing NaN or infinity elements.");
72      else {
73        tMeanCalculator.Add(target);
74        yVarianceCalculator.Add(x);
75        ytCovarianceEvaluator.Add(x, target);
76
77        n++;
78      }
79    }
80  }
81}
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