1 | #ifndef _theplu_yat_statistics_averager_ |
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2 | #define _theplu_yat_statistics_averager_ |
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3 | |
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4 | // $Id: Averager.h 916 2007-09-30 00:50:10Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004 Jari Häkkinen, Peter Johansson |
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8 | Copyright (C) 2005, 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson |
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9 | Copyright (C) 2007 Peter Johansson |
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10 | |
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11 | This file is part of the yat library, http://trac.thep.lu.se/trac/yat |
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12 | |
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13 | The yat library is free software; you can redistribute it and/or |
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14 | modify it under the terms of the GNU General Public License as |
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15 | published by the Free Software Foundation; either version 2 of the |
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16 | License, or (at your option) any later version. |
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17 | |
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18 | The yat library is distributed in the hope that it will be useful, |
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19 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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20 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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21 | General Public License for more details. |
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22 | |
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23 | You should have received a copy of the GNU General Public License |
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24 | along with this program; if not, write to the Free Software |
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25 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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26 | 02111-1307, USA. |
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27 | */ |
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28 | |
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29 | #include "yat/utility/IteratorTraits.h" |
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30 | |
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31 | #include <cmath> |
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32 | #include <sys/types.h> |
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33 | |
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34 | namespace theplu{ |
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35 | namespace yat{ |
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36 | namespace statistics{ |
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37 | |
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38 | class ostream; |
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39 | |
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40 | /// |
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41 | /// @brief Class to calculate simple (first and second moments) averages. |
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42 | /// |
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43 | /// @see AveragerWeighted AveragerPair AveragerPairWeighted |
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44 | /// |
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45 | class Averager |
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46 | { |
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47 | public: |
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48 | |
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49 | /// |
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50 | /// Default constructor |
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51 | /// |
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52 | Averager(void); |
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53 | |
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54 | /// |
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55 | /// Constructor taking sum of \a x, sum of squared x, \a xx, and |
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56 | /// number of samples \a n. |
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57 | /// |
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58 | Averager(double x, double xx, u_long n); |
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59 | |
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60 | /// |
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61 | /// Copy constructor |
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62 | /// |
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63 | Averager(const Averager& a); |
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64 | |
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65 | /// |
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66 | /// Adding \a n (default=1) number of data point(s) with value \a d. |
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67 | /// |
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68 | void add(double d, u_long n=1); |
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69 | |
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70 | /** |
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71 | @brief Coeffient of variation |
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72 | |
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73 | Coeffient of variation (cv) is defined as ratio between the |
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74 | standard deviation and the mean: \f$ \frac{\sigma}{\mu} \f$. |
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75 | |
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76 | @return standard deviation divided by mean. |
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77 | */ |
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78 | double cv(void) const; |
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79 | |
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80 | /// |
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81 | /// @return Mean of presented data, \f$ \frac{1}{n}\sum x_i \f$ |
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82 | /// |
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83 | double mean(void) const; |
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84 | |
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85 | /// |
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86 | /// @return Number of data points |
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87 | /// |
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88 | u_long n(void) const; |
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89 | |
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90 | /// |
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91 | /// @brief Rescales the object |
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92 | /// |
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93 | /// \f$ \forall x_i \rightarrow a*x_i \f$, |
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94 | /// \f$ \forall x_i^2 \rightarrow a^2*x_i^2 \f$ |
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95 | /// |
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96 | void rescale(double a); |
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97 | |
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98 | /// |
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99 | /// @return Standard error, i.e. standard deviation of the mean |
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100 | /// \f$ \sqrt{variance()/n} \f$ |
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101 | /// |
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102 | double standard_error(void) const; |
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103 | |
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104 | /// |
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105 | /// @brief The standard deviation is defined as the square root of |
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106 | /// the variance. |
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107 | /// |
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108 | /// @return The standard deviation, root of the variance(). |
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109 | /// |
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110 | double std(void) const; |
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111 | |
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112 | /// |
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113 | /// @brief The standard deviation is defined as the square root of |
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114 | /// the variance. |
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115 | /// |
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116 | /// @return Standard deviation around \a m, root of the variance(m). |
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117 | /// |
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118 | double std(double m) const; |
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119 | |
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120 | /// |
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121 | /// @return The sum of x |
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122 | /// |
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123 | double sum_x(void) const; |
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124 | |
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125 | /// |
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126 | /// @return The sum of squares |
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127 | /// |
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128 | double sum_xx(void) const; |
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129 | |
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130 | /// |
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131 | /// @return \f$ \sum_i (x_i-m)^2 \f$ |
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132 | /// |
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133 | double sum_xx_centered(void) const; |
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134 | |
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135 | /// |
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136 | /// @brief The variance with know mean |
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137 | /// |
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138 | /// The variance is calculated as |
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139 | /// \f$ \frac{1}{n}\sum (x_i-m)^2 \f$. |
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140 | /// |
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141 | /// @return Variance when the mean is known to be \a m. |
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142 | /// |
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143 | double variance(double m) const; |
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144 | |
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145 | /// |
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146 | /// @brief The estimated variance |
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147 | /// |
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148 | /// The variance is calculated as \f$ \frac{1}{N}\sum_i |
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149 | /// (x_i-m)^2 \f$, where \f$ m \f$ is the mean. |
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150 | /// |
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151 | /// @return Estimation of variance |
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152 | /// |
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153 | double variance(void) const; |
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154 | |
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155 | /// |
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156 | /// The variance is calculated using the \f$ (n-1) \f$ correction, |
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157 | /// which means it is the best unbiased estimator of the variance |
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158 | /// \f$ \frac{1}{N-1}\sum_i (x_i-m)^2 \f$, where \f$ m \f$ is the |
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159 | /// mean. |
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160 | /// |
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161 | /// @return unbiased estimation of variance |
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162 | /// |
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163 | double variance_unbiased(void) const; |
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164 | |
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165 | /// |
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166 | /// @brief Reset everything to zero |
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167 | /// |
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168 | void reset(void); |
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169 | |
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170 | /// |
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171 | /// @brief The assignment operator |
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172 | /// |
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173 | const Averager& operator=(const Averager&); |
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174 | |
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175 | /// |
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176 | /// Operator to add another Averager |
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177 | /// |
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178 | const Averager& operator+=(const Averager&); |
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179 | |
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180 | private: |
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181 | u_long n_; |
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182 | double x_, xx_; |
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183 | }; |
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184 | |
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185 | |
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186 | /** |
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187 | \brief adding a ranges of values to Averager \a a |
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188 | */ |
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189 | template <typename Iter> |
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190 | void add(Averager& a, Iter first, Iter last) |
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191 | { |
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192 | utility::check_iterator_is_unweighted(first); |
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193 | for ( ; first != last; ++first) |
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194 | a.add(*first); |
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195 | } |
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196 | |
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197 | }}} // of namespace statistics, yat, and theplu |
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198 | |
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199 | #endif |
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