1 | // $Id: Averager.h 349 2005-06-08 19:11:22Z peter $ |
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2 | |
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3 | #ifndef _theplu_statistics_averager_ |
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4 | #define _theplu_statistics_averager_ |
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5 | |
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6 | #include <cmath> |
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7 | |
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8 | namespace theplu{ |
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9 | namespace gslapi{ |
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10 | class vector; |
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11 | } |
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12 | |
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13 | namespace statistics{ |
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14 | class ostream; |
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15 | |
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16 | /// |
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17 | /// Class to calculate simple (first and second moments) averages. |
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18 | /// |
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19 | /// @see AveragerWeighted |
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20 | /// |
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21 | class Averager |
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22 | { |
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23 | public: |
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24 | |
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25 | /// |
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26 | /// Default constructor |
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27 | /// |
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28 | Averager(void); |
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29 | |
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30 | /// |
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31 | /// Constructor taking sum of \a x, sum of squared x, \a xx, and |
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32 | /// number of samples \a n. |
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33 | /// |
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34 | Averager(const double x, const double xx, const long n); |
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35 | |
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36 | /// |
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37 | /// Copy constructor |
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38 | /// |
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39 | Averager(const Averager&); |
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40 | |
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41 | /// |
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42 | /// Adding \a n (default=1) number of data point(s) with value \a d. |
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43 | /// |
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44 | inline void add(const double d,const long n=1) |
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45 | {n_+=n; x_+=n*d; xx_+=n*d*d;} |
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46 | |
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47 | /// |
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48 | /// @return Mean of presented data, \f$ \frac{1}{n}\sum x_i \f$ |
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49 | /// |
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50 | inline double mean(void) const { return n_ ? x_/n_ : 0; } |
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51 | |
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52 | /// |
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53 | /// @return Mean of squared values \f$ \frac{1}{n}\sum x_i^2 \f$. |
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54 | /// |
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55 | inline double mean_sqr(void) const { return n_ ? xx_/n_ : 0; } |
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56 | |
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57 | /// |
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58 | /// @return Number of data points |
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59 | /// |
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60 | inline long n(void) const { return n_; } |
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61 | |
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62 | /// |
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63 | /// Rescales the object, \f$ \forall x_i \rightarrow a*x_i\f$, \f$ |
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64 | /// \forall x_i^2 \rightarrow a^2*x_i^2 \f$ |
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65 | /// |
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66 | inline void rescale(double a) { x_*=a; xx_*=a*a; } |
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67 | |
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68 | /// |
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69 | /// Resets everything to zero |
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70 | /// |
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71 | inline void reset(void) { n_=0; x_=xx_=0.0;} |
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72 | |
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73 | /// |
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74 | /// The standard deviation is defined as the square root of the |
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75 | /// variance. |
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76 | /// |
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77 | /// @return The standard deviation, root of the variance(). |
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78 | /// |
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79 | inline double std(void) const { return sqrt(variance()); } |
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80 | |
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81 | /// |
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82 | /// @return Standard error, i.e. standard deviation of the mean |
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83 | /// \f$ \sqrt{variance()/n} \f$ |
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84 | /// |
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85 | inline double standard_error(void) const { return sqrt(variance()/n_); } |
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86 | |
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87 | /// |
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88 | /// @return The sum of x |
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89 | /// |
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90 | inline double sum_x(void) const { return x_; } |
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91 | |
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92 | /// |
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93 | /// @return The sum of squares |
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94 | /// |
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95 | inline double sum_xsqr(void) const { return xx_; } |
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96 | |
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97 | /// |
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98 | /// @return \f$ \sum_i (x_i-m)^2\f$ |
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99 | /// |
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100 | inline double sum_xsqr_centered(void) const { return xx_-x_*x_/n_; } |
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101 | |
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102 | /// |
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103 | /// The variance is calculated using the \f$ (n-1) \f$ correction, |
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104 | /// which means it is the best unbiased estimator of the variance |
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105 | /// \f$ \frac{1}{N-1}\sum_i (x_i-m)^2\f$, where \f$m\f$ is the |
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106 | /// mean. |
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107 | /// |
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108 | /// @return The variance |
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109 | /// |
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110 | inline double variance(void) const |
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111 | { return (n_>1) ? sum_xsqr_centered()/(n_-1) : 0; } |
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112 | |
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113 | /// |
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114 | /// The assignment operator |
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115 | /// |
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116 | inline const Averager& operator=(const Averager& a) |
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117 | { n_=a.n_; x_=a.x_; xx_=a.xx_; return *this; } |
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118 | |
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119 | /// |
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120 | /// Operator to add another Averager |
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121 | /// |
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122 | const Averager& operator+=(const Averager&); |
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123 | |
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124 | private: |
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125 | long n_; |
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126 | double x_, xx_; |
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127 | }; |
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128 | |
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129 | }} // of namespace statistics and namespace theplu |
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130 | |
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131 | #endif |
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