1 | #ifndef _theplu_yat_statistics_averagerpairweighted_ |
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2 | #define _theplu_yat_statistics_averagerpairweighted_ |
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3 | |
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4 | // $Id: AveragerPairWeighted.h 1122 2008-02-22 17:01:15Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2005 Markus Ringnér, Peter Johansson |
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8 | Copyright (C) 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/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 "AveragerWeighted.h" |
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30 | |
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31 | #include "yat/utility/iterator_traits.h" |
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32 | #include "yat/utility/yat_assert.h" |
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33 | |
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34 | #include <cmath> |
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35 | #include <stdexcept> |
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36 | |
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37 | namespace theplu{ |
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38 | namespace yat{ |
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39 | namespace statistics{ |
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40 | /// |
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41 | /// @brief Class for taking care of mean and covariance of two variables in |
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42 | /// a weighted manner. |
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43 | /// |
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44 | /// <a href="Statistics/index.html">Weighted Statistics document</a> |
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45 | /// |
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46 | /// If nothing else stated, each function fulfills the |
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47 | /// following:<br> <ul><li>Setting a weight to zero corresponds to |
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48 | /// removing the data point from the dataset.</li><li> Setting all |
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49 | /// weights to unity, the yields the same result as from |
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50 | /// corresponding function in AveragerPair.</li><li> Rescaling weights |
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51 | /// does not change the performance of the object.</li></ul> |
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52 | /// |
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53 | /// @see Averager AveragerWeighted AveragerPair |
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54 | /// |
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55 | class AveragerPairWeighted |
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56 | { |
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57 | public: |
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58 | |
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59 | /// |
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60 | /// @brief The default constructor |
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61 | /// |
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62 | AveragerPairWeighted(void); |
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63 | |
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64 | /// |
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65 | /// Adding a pair of data points with value \a x and \a y, and |
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66 | /// their weights. If either of the weights are zero the addition |
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67 | /// is ignored |
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68 | /// |
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69 | void add(const double x, const double y, |
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70 | const double wx, const double wy); |
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71 | |
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72 | /// |
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73 | /// @brief Pearson correlation coefficient. |
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74 | /// |
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75 | /// @return \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sqrt{\sum |
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76 | /// w_xw_y (y-m_y)^2\sum w_xw_y (y-m_y)^2}} \f$ where m is |
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77 | /// calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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78 | /// |
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79 | double correlation(void) const; |
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80 | |
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81 | /// |
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82 | /// \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sum w_xw_y} \f$ where m |
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83 | /// is calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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84 | /// |
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85 | double covariance(void) const; |
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86 | |
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87 | /** |
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88 | @return \f$ \frac{\sum w_xw_y(x-y)^2}{\sum w_xw_y} \f$ |
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89 | */ |
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90 | double msd(void) const; |
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91 | |
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92 | /** |
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93 | @return \f$ \frac{\left(\sum w_x w_y\right)^2}{\sum w_x^2w_y^2} \f$ |
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94 | */ |
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95 | double n(void) const; |
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96 | |
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97 | /// |
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98 | /// @brief Reset everything to zero |
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99 | /// |
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100 | void reset(void); |
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101 | |
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102 | /// |
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103 | /// @return \f$ \sum w_xw_y \f$ |
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104 | /// |
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105 | double sum_w(void) const; |
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106 | |
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107 | /// |
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108 | /// @return \f$ \sum w_xw_yxy \f$ |
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109 | /// |
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110 | double sum_xy(void) const; |
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111 | |
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112 | /// |
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113 | /// @return \f$ \sum w_xw_y (x-m_x)(y-m_y) \f$ where m is calculated as |
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114 | /// \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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115 | /// |
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116 | double sum_xy_centered(void) const; |
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117 | |
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118 | /// |
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119 | /// @note the weights are calculated as \f$ w = w_x * w_y \f$ |
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120 | /// |
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121 | /// @return AveragerWeighted for x |
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122 | /// |
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123 | const AveragerWeighted& x_averager(void) const; |
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124 | |
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125 | /// |
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126 | /// @note the weights are calculated as \f$ w = w_x * w_y \f$ |
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127 | /// |
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128 | /// @return AveragerWeighted for y |
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129 | /// |
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130 | const AveragerWeighted& y_averager(void) const; |
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131 | |
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132 | private: |
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133 | AveragerWeighted x_; // weighted averager with w = w_x*w_y |
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134 | AveragerWeighted y_; // weighted averager with w = w_x*w_y |
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135 | double wxy_; |
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136 | double w_; |
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137 | |
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138 | }; |
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139 | |
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140 | /** |
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141 | \brief adding a ranges of values to AveragerPairWeighted \a ap |
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142 | */ |
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143 | template <class Iter1, class Iter2> |
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144 | void add(AveragerPairWeighted& ap, Iter1 first1, Iter1 last1, Iter2 first2) |
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145 | { |
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146 | for ( ; first1 != last1; ++first1, ++first2) { |
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147 | ap.add(utility::iterator_traits<Iter1>().data(first1), |
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148 | utility::iterator_traits<Iter2>().data(first2), |
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149 | utility::iterator_traits<Iter1>().weight(first1), |
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150 | utility::iterator_traits<Iter2>().weight(first2)); |
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151 | } |
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152 | } |
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153 | |
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154 | |
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155 | /** |
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156 | \brief adding four ranges of values to AveragerPairWeighted \a ap |
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157 | */ |
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158 | template <class Iter1, class Iter2, class Iter3, class Iter4> |
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159 | void add(AveragerPairWeighted& ap, Iter1 x, Iter1 xlast, Iter2 y, Iter3 wx, |
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160 | Iter4 wy) |
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161 | { |
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162 | utility::check_iterator_is_unweighted(x); |
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163 | utility::check_iterator_is_unweighted(y); |
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164 | utility::check_iterator_is_unweighted(wx); |
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165 | utility::check_iterator_is_unweighted(wy); |
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166 | while (x!=xlast) { |
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167 | ap.add(*x, *y, *wx, *wy); |
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168 | ++x; |
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169 | ++y; |
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170 | ++wx; |
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171 | ++wy; |
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172 | } |
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173 | } |
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174 | |
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175 | |
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176 | }}} // of namespace statistics, yat, and theplu |
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177 | |
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178 | #endif |
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