# source:trunk/lib/statistics/AveragerPair.h@313

Last change on this file since 313 was 313, checked in by Peter, 17 years ago

doc corrected

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1// $Id: AveragerPair.h 313 2005-05-12 07:57:50Z peter$
2
3#ifndef _theplu_statistics_averagerpair_
4#define _theplu_statistics__averagerpair_
5
6#include <c++_tools/statistics/Averager.h>
7
8#include <cmath>
9#include <utility>
10
11
12namespace theplu{
13
14  class gslapi::vector;
15
16namespace statistics{
17  ///
18  /// Class for taking care of mean and covariance of two variables.
19  ///
20  class AveragerPair
21  {
22  public:
23
24    ///
25    /// Default constructor
26    ///
27    AveragerPair(void);
28
29    ///
30    /// Constructor taking sum of \a x , \a xx , \a y , \a yy , xy and
31    /// number of pair of values \a n
32    ///
33    AveragerPair(const double x, const double xx, const double y,
34                 const double yy, const double xy, const long);
35
36    ///
37    /// Copy constructor
38    ///
39    AveragerPair(const AveragerPair&);
40
41    ///
42    /// Adding \a n pairs of data points with value \a x and \a y.
43    ///
44    inline void add(const double x, const double y, const long n=1)
46
47    ///
48    /// \f$\frac{\sum_i (x_i-m_x)(y_i-m_y)}{\sum_i 49 /// (x_i-m_x)^2+\sum_i (y_i-m_y)^2 + n(m_x-m_y)^2}\f$
50    ///
51    /// @return Concordence correlation coefficient.
52    ///
53    inline double ccc(void) const
54      { return ( (x_.variance() || y_.variance() || x_.mean()-y_.mean() ) ?
55                 ((2*covariance()) /
56                  ((x_.variance()+y_.variance()) +
57                   (x_.mean()-y_.mean())*(x_.mean()-y_.mean()))) : 0); }
58
59    ///
60    /// \f$\frac{\sum_i (x_i-m_x)(y_i-m_y)}{\sqrt{\sum_i 61 /// (x_i-m_x)^2\sum_i (y_i-m_y)^2}}\f$
62    ///
63    /// @return Pearson correlation coefficient.
64    ///
65    inline double correlation(void) const
66      { return ((x_.std()>0 && y_.std()>0) ?
67                (covariance() / (x_.std()*y_.std()) ) : 0); }
68
69    ///
70    /// The covariance is calculated using the \f$(n-1) \f$
71    /// correction, which means it is the best unbiased estimator of
72    /// the covariance \f$\frac{1}{N-1}\sum_i (x_i-m_x)(y_i-m_y)\f$,
73    /// where \f$m\f$ is the mean.
74    ///
75    /// @return The covariance.
76    ///
77    inline double covariance(void) const
78      { return (n()>1) ? (xy_ - x_.sum_x()*y_.mean()) / (n()-1): 0; }
79
80    ///
81    /// @return The mean of xy.
82    ///
83    inline double mean_xy(void) const { return xy_/n(); }
84
85    ///
86    /// @return Average squared deviation between x and y \f$87 /// \frac{1}{N} \sum (x-y)^2 \f$
88    ///
89    inline double msd() const {return x_.mean_sqr()+y_.mean_sqr()-2*mean_xy();}
90
91    ///
92    /// @return The number of pair of data points.
93    ///
94    inline long n(void) const { return x_.n(); }
95
96    ///
97    /// Resets everything to zero
98    ///
99    inline void reset(void) { x_.reset(); y_.reset(); xy_=0.0; }
100
101    ///
102    /// @return The sum of xy.
103    ///
104    inline double sum_xy(void) const { return xy_; }
105
106    ///
107    /// @return \f$\sum_i (x_i-m_x)(y_i-m_y)\f$
108    ///
109    inline double sum_xy_centered(void) const {return xy_-x_.sum_x()*y_.mean();}
110
111    ///
112    /// @return A const refencer to the averager object for x.
113    ///
114    inline const Averager& x_averager(void) const { return x_; }
115
116    ///
117    /// @return A const reference to the averager object for y
118    ///
119    inline const Averager& y_averager(void) const { return y_; }
120
121    ///
122    /// The assigment operator
123    ///
124    inline const AveragerPair& operator=(const AveragerPair& a)
125      { x_=a.x_; y_=a.y_; xy_=a.xy_; return *this; }
126
127    ///
128    /// Operator to add another Averager
129    ///
130    const AveragerPair& operator+=(const AveragerPair&);
131
132  private:
133    Averager x_;
134    Averager y_;
135    double  xy_;
136
137  };
138
139}} // of namespace statistics and namespace theplu
140
141#endif
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