source: trunk/lib/statistics/AveragerPairWeighted.h @ 490

Last change on this file since 490 was 490, checked in by Peter, 17 years ago

added tests for AveragerPairWeighted?, corrected docs, and added an
add(vector) for AvergaerPair?

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 3.1 KB
Line 
1// $Id: AveragerPairWeighted.h 490 2006-01-04 20:32:05Z peter $
2
3#ifndef _theplu_statistics_averager_pair_weighted_
4#define _theplu_statistics_averager_pair_weighted_
5
6#include <c++_tools/statistics/AveragerWeighted.h>
7#include <c++_tools/gslapi/vector.h>
8
9#include <cmath>
10//#include <utility>
11
12
13namespace theplu{
14namespace statistics{
15  ///
16  /// Class for taking care of mean and covariance of two variables in
17  /// a weighted manner.
18  ///
19  /// <a href="../Statistics/index.html">Weighted Statistics document</a>
20  ///
21  /// If nothing else stated, each function fulfills the
22  /// following:<br> <ul><li>Setting a weight to zero corresponds to
23  /// removing the data point from the dataset.</li><li> Setting all
24  /// weights to unity, the yields the same result as from
25  /// corresponding function in AveragerPair.</li><li> Rescaling weights
26  /// does not change the performance of the object.</li></ul>
27  ///
28  /// @see Averager AveragerWeighted AveragerPair
29  ///
30  class AveragerPairWeighted
31  {
32  public:
33
34    AveragerPairWeighted()
35      : wxy_(0), w_(0)
36    {
37    }
38
39    ///
40    /// Adding a pair of data points with value \a x and \a y, and
41    /// their weights. If either of the weights are zero the addition
42    /// is ignored
43    ///
44    void  add(const double x, const double y, 
45              const double wx, const double wy);
46
47    ///
48    /// Adding pair of data and corresponding weight in vectors
49    ///
50    void add(const gslapi::vector& x, 
51             const gslapi::vector& y, 
52             const gslapi::vector& wx, 
53             const gslapi::vector& wy);
54
55    ///
56    /// @brief Pearson correlation coefficient.
57    ///
58    /// @return \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sqrt{\sum
59    /// w_xw_y (y-m_y)^2\sum w_xw_y (y-m_y)^2}} \f$ where m is
60    /// calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$
61    ///
62    inline double correlation(void) const 
63    { return covariance() / ( x_.std()*y_.std() ); }
64 
65    ///
66    /// \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sum w_xw_y} \f$ where m
67    /// is calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$
68    ///
69    inline double covariance(void) const { return sum_xy_centered()/sum_w(); }
70
71    ///
72    /// reset everything to zero
73    ///
74    inline void reset(void) { x_.reset(); y_.reset(); wxy_=0; w_=0; }
75
76    ///
77    /// @return \f$ \sum w_xw_y \f$
78    ///
79    inline double sum_w(void) const { return w_; }
80
81    ///
82    /// @return \f$ \sum w_xw_yxy \f$
83    ///
84    inline double sum_xy(void) const { return wxy_; }
85
86    ///
87    /// @return \f$ \sum w_xw_y (x-m_x)(y-m_y) \f$ where m is calculated as
88    /// \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$
89    ///
90    inline double sum_xy_centered(void) const 
91    { return sum_xy() - x_.sum_wx()*y_.mean(); }
92
93    ///
94    /// @note the weights are calculated as \f$ w = w_x * w_y \f$
95    ///
96    /// @return AveragerWeighted for x
97    ///
98    inline const AveragerWeighted& x_averager(void) const { return x_; }
99
100    ///
101    /// @note the weights are calculated as \f$ w = w_x * w_y \f$
102    ///
103    /// @return AveragerWeighted for y
104    ///
105    inline const AveragerWeighted& y_averager(void) const { return y_; }
106
107  private:
108    AveragerWeighted x_; // weighted averager with w = w_x*w_y
109    AveragerWeighted y_; // weighted averager with w = w_x*w_y
110    double wxy_;
111    double w_;
112
113  };
114
115}} // of namespace statistics and namespace theplu
116
117#endif
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