1 | // $Id: AveragerPairWeighted.h 494 2006-01-10 13:44:14Z peter $ |
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2 | |
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3 | #ifndef _theplu_statistics_averager_pair_weighted_ |
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4 | #define _theplu_statistics_averager_pair_weighted_ |
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5 | |
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6 | #include <c++_tools/statistics/AveragerWeighted.h> |
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7 | #include <c++_tools/gslapi/vector.h> |
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8 | |
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9 | #include <cmath> |
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10 | //#include <utility> |
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11 | |
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12 | |
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13 | namespace theplu{ |
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14 | namespace statistics{ |
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15 | /// |
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16 | /// Class for taking care of mean and covariance of two variables in |
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17 | /// a weighted manner. |
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18 | /// |
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19 | /// <a href="Statistics/index.html">Weighted Statistics document</a> |
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20 | /// |
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21 | /// If nothing else stated, each function fulfills the |
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22 | /// following:<br> <ul><li>Setting a weight to zero corresponds to |
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23 | /// removing the data point from the dataset.</li><li> Setting all |
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24 | /// weights to unity, the yields the same result as from |
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25 | /// corresponding function in AveragerPair.</li><li> Rescaling weights |
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26 | /// does not change the performance of the object.</li></ul> |
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27 | /// |
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28 | /// @see Averager AveragerWeighted AveragerPair |
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29 | /// |
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30 | class AveragerPairWeighted |
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31 | { |
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32 | public: |
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33 | |
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34 | AveragerPairWeighted() |
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35 | : wxy_(0), w_(0) |
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36 | { |
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37 | } |
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38 | |
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39 | /// |
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40 | /// Adding a pair of data points with value \a x and \a y, and |
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41 | /// their weights. If either of the weights are zero the addition |
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42 | /// is ignored |
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43 | /// |
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44 | void add(const double x, const double y, |
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45 | const double wx, const double wy); |
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46 | |
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47 | /// |
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48 | /// Adding pair of data and corresponding weight in vectors |
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49 | /// |
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50 | void add(const gslapi::vector& x, |
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51 | const gslapi::vector& y, |
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52 | const gslapi::vector& wx, |
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53 | const gslapi::vector& wy); |
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54 | |
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55 | /// |
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56 | /// @brief Pearson correlation coefficient. |
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57 | /// |
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58 | /// @return \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sqrt{\sum |
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59 | /// w_xw_y (y-m_y)^2\sum w_xw_y (y-m_y)^2}} \f$ where m is |
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60 | /// calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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61 | /// |
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62 | inline double correlation(void) const |
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63 | { return covariance() / ( x_.std()*y_.std() ); } |
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64 | |
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65 | /// |
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66 | /// \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sum w_xw_y} \f$ where m |
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67 | /// is calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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68 | /// |
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69 | inline double covariance(void) const { return sum_xy_centered()/sum_w(); } |
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70 | |
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71 | /// |
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72 | /// reset everything to zero |
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73 | /// |
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74 | inline void reset(void) { x_.reset(); y_.reset(); wxy_=0; w_=0; } |
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75 | |
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76 | /// |
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77 | /// @return \f$ \sum w_xw_y \f$ |
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78 | /// |
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79 | inline double sum_w(void) const { return w_; } |
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80 | |
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81 | /// |
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82 | /// @return \f$ \sum w_xw_yxy \f$ |
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83 | /// |
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84 | inline double sum_xy(void) const { return wxy_; } |
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85 | |
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86 | /// |
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87 | /// @return \f$ \sum w_xw_y (x-m_x)(y-m_y) \f$ where m is calculated as |
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88 | /// \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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89 | /// |
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90 | inline double sum_xy_centered(void) const |
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91 | { return sum_xy() - x_.sum_wx()*y_.mean(); } |
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92 | |
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93 | /// |
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94 | /// @note the weights are calculated as \f$ w = w_x * w_y \f$ |
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95 | /// |
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96 | /// @return AveragerWeighted for x |
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97 | /// |
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98 | inline const AveragerWeighted& x_averager(void) const { return x_; } |
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99 | |
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100 | /// |
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101 | /// @note the weights are calculated as \f$ w = w_x * w_y \f$ |
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102 | /// |
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103 | /// @return AveragerWeighted for y |
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104 | /// |
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105 | inline const AveragerWeighted& y_averager(void) const { return y_; } |
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106 | |
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107 | private: |
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108 | AveragerWeighted x_; // weighted averager with w = w_x*w_y |
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109 | AveragerWeighted y_; // weighted averager with w = w_x*w_y |
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110 | double wxy_; |
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111 | double w_; |
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112 | |
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113 | }; |
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114 | |
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115 | }} // of namespace statistics and namespace theplu |
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116 | |
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117 | #endif |
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