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 683 2006-10-11 22:20:36Z jari $ |
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5 | |
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6 | /* |
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7 | Copyright (C) The authors contributing to this file. |
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8 | |
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9 | This file is part of the yat library, http://lev.thep.lu.se/trac/yat |
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10 | |
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11 | The yat library is free software; you can redistribute it and/or |
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12 | modify it under the terms of the GNU General Public License as |
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13 | published by the Free Software Foundation; either version 2 of the |
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14 | License, or (at your option) any later version. |
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15 | |
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16 | The yat library is distributed in the hope that it will be useful, |
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17 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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18 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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19 | General Public License for more details. |
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20 | |
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21 | You should have received a copy of the GNU General Public License |
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22 | along with this program; if not, write to the Free Software |
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23 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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24 | 02111-1307, USA. |
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25 | */ |
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26 | |
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27 | #include "AveragerWeighted.h" |
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28 | |
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29 | #include <cmath> |
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30 | |
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31 | namespace theplu{ |
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32 | namespace yat{ |
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33 | namespace classifier{ |
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34 | class DataLookup1D; |
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35 | class DataLookupWeighted1D; |
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36 | } |
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37 | namespace statistics{ |
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38 | /// |
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39 | /// Class for taking care of mean and covariance of two variables in |
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40 | /// a weighted manner. |
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41 | /// |
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42 | /// <a href="Statistics/index.html">Weighted Statistics document</a> |
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43 | /// |
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44 | /// If nothing else stated, each function fulfills the |
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45 | /// following:<br> <ul><li>Setting a weight to zero corresponds to |
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46 | /// removing the data point from the dataset.</li><li> Setting all |
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47 | /// weights to unity, the yields the same result as from |
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48 | /// corresponding function in AveragerPair.</li><li> Rescaling weights |
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49 | /// does not change the performance of the object.</li></ul> |
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50 | /// |
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51 | /// @see Averager AveragerWeighted AveragerPair |
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52 | /// |
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53 | class AveragerPairWeighted |
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54 | { |
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55 | public: |
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56 | |
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57 | AveragerPairWeighted() |
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58 | : wxy_(0), w_(0) |
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59 | { |
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60 | } |
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61 | |
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62 | /// |
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63 | /// Adding a pair of data points with value \a x and \a y, and |
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64 | /// their weights. If either of the weights are zero the addition |
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65 | /// is ignored |
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66 | /// |
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67 | void add(const double x, const double y, |
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68 | const double wx, const double wy); |
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69 | |
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70 | /// |
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71 | /// Adding two sequences of data @a x and @a y. The data |
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72 | /// should be paired so \f$ x(i) \f$ is associated to \f$ y(i) \f$ |
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73 | /// @a x will be treated as having all weights equal to unity |
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74 | /// |
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75 | void add(const classifier::DataLookup1D& x, |
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76 | const classifier::DataLookupWeighted1D& y); |
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77 | |
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78 | /// |
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79 | /// Adding two sequences of data @a x and @a y. The data should be |
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80 | /// paired so \f$ x(i) \f$ is associated to \f$ y(i) \f$ |
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81 | /// @a y will be treated as having all weights equal to unity |
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82 | /// |
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83 | inline void add(const classifier::DataLookupWeighted1D& x, |
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84 | const classifier::DataLookup1D& y){ add(y,x); } |
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85 | |
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86 | /// |
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87 | /// Adding two sequences of weighted data @a x and @a y. The data |
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88 | /// should be paired so \f$ x(i) \f$ is associated to \f$ y(i) \f$ |
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89 | /// |
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90 | void add(const classifier::DataLookupWeighted1D& x, |
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91 | const classifier::DataLookupWeighted1D& y); |
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92 | |
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93 | /// |
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94 | /// Adding pair of data and corresponding pair of weights in arrays |
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95 | /// |
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96 | /// |
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97 | /// The requirements for the types T1, T2, T3 and T4 of the arrays |
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98 | /// are: operator[] returning an element and function /// size() |
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99 | /// returning the number of elements. |
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100 | /// |
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101 | template <typename T1,typename T2,typename T3,typename T4> |
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102 | void add_values(const T1& x, |
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103 | const T2& y, |
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104 | const T3& wx, |
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105 | const T4& wy); |
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106 | |
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107 | /// |
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108 | /// @brief Pearson correlation coefficient. |
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109 | /// |
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110 | /// @return \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sqrt{\sum |
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111 | /// w_xw_y (y-m_y)^2\sum w_xw_y (y-m_y)^2}} \f$ where m is |
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112 | /// calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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113 | /// |
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114 | inline double correlation(void) const |
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115 | { return covariance() / ( x_.std()*y_.std() ); } |
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116 | |
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117 | /// |
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118 | /// \f$ \frac{\sum w_xw_y (x-m_x)(y-m_y)}{\sum w_xw_y} \f$ where m |
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119 | /// is calculated as \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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120 | /// |
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121 | inline double covariance(void) const { return sum_xy_centered()/sum_w(); } |
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122 | |
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123 | /// |
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124 | /// reset everything to zero |
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125 | /// |
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126 | inline void reset(void) { x_.reset(); y_.reset(); wxy_=0; w_=0; } |
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127 | |
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128 | /// |
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129 | /// @return \f$ \sum w_xw_y \f$ |
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130 | /// |
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131 | inline double sum_w(void) const { return w_; } |
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132 | |
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133 | /// |
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134 | /// @return \f$ \sum w_xw_yxy \f$ |
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135 | /// |
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136 | inline double sum_xy(void) const { return wxy_; } |
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137 | |
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138 | /// |
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139 | /// @return \f$ \sum w_xw_y (x-m_x)(y-m_y) \f$ where m is calculated as |
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140 | /// \f$ m_x = \frac {\sum w_xw_yx}{\sum w} \f$ |
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141 | /// |
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142 | inline double sum_xy_centered(void) const |
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143 | { return sum_xy() - x_.sum_wx()*y_.mean(); } |
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144 | |
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145 | /// |
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146 | /// @note the weights are calculated as \f$ w = w_x * w_y \f$ |
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147 | /// |
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148 | /// @return AveragerWeighted for x |
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149 | /// |
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150 | inline const AveragerWeighted& x_averager(void) const { return x_; } |
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151 | |
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152 | /// |
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153 | /// @note the weights are calculated as \f$ w = w_x * w_y \f$ |
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154 | /// |
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155 | /// @return AveragerWeighted for y |
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156 | /// |
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157 | inline const AveragerWeighted& y_averager(void) const { return y_; } |
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158 | |
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159 | private: |
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160 | AveragerWeighted x_; // weighted averager with w = w_x*w_y |
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161 | AveragerWeighted y_; // weighted averager with w = w_x*w_y |
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162 | double wxy_; |
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163 | double w_; |
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164 | |
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165 | }; |
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166 | |
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167 | template <typename T1,typename T2,typename T3,typename T4> |
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168 | void AveragerPairWeighted::add_values(const T1& x, |
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169 | const T2& y, |
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170 | const T3& wx, |
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171 | const T4& wy) |
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172 | { |
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173 | for (size_t i=0; i<x.size(); i++) |
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174 | add(x[i],y[i],wx[i],wy[i]); |
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175 | } |
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176 | |
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177 | }}} // of namespace statistics, yat, and theplu |
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178 | |
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179 | #endif |
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