1 | // $Id: LinearWeighted.cc 1020 2008-02-01 17:17:26Z peter $ |
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2 | |
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3 | /* |
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4 | Copyright (C) 2005 Peter Johansson |
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5 | Copyright (C) 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson |
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6 | Copyright (C) 2007 Jari Häkkinen, Peter Johansson |
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7 | |
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8 | This file is part of the yat library, http://trac.thep.lu.se/yat |
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9 | |
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10 | The yat library is free software; you can redistribute it and/or |
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11 | modify it under the terms of the GNU General Public License as |
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12 | published by the Free Software Foundation; either version 2 of the |
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13 | License, or (at your option) any later version. |
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14 | |
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15 | The yat library is distributed in the hope that it will be useful, |
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16 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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17 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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18 | General Public License for more details. |
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19 | |
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20 | You should have received a copy of the GNU General Public License |
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21 | along with this program; if not, write to the Free Software |
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22 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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23 | 02111-1307, USA. |
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24 | */ |
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25 | |
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26 | #include "LinearWeighted.h" |
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27 | #include "yat/statistics/AveragerPairWeighted.h" |
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28 | #include "yat/utility/vector.h" |
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29 | |
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30 | #include <cassert> |
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31 | |
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32 | namespace theplu { |
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33 | namespace yat { |
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34 | namespace regression { |
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35 | |
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36 | LinearWeighted::LinearWeighted(void) |
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37 | : OneDimensionalWeighted(), alpha_(0), alpha_var_(0), beta_(0), |
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38 | beta_var_(0) |
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39 | { |
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40 | } |
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41 | |
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42 | LinearWeighted::~LinearWeighted(void) |
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43 | { |
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44 | } |
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45 | |
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46 | double LinearWeighted::alpha(void) const |
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47 | { |
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48 | return alpha_; |
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49 | } |
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50 | |
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51 | double LinearWeighted::alpha_var(void) const |
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52 | { |
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53 | return alpha_var_; |
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54 | } |
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55 | |
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56 | double LinearWeighted::beta(void) const |
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57 | { |
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58 | return beta_; |
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59 | } |
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60 | |
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61 | double LinearWeighted::beta_var(void) const |
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62 | { |
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63 | return beta_var_; |
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64 | } |
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65 | |
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66 | void LinearWeighted::fit(const utility::VectorBase& x, |
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67 | const utility::VectorBase& y, |
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68 | const utility::VectorBase& w) |
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69 | { |
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70 | assert(x.size()==y.size()); |
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71 | assert(x.size()==w.size()); |
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72 | |
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73 | // AveragerPairWeighted requires 2 weights but works only on the |
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74 | // product wx*wy, so we can send in w and a dummie to get what we |
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75 | // want. |
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76 | ap_.reset(); |
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77 | ap_.add_values(x,y,utility::vector(x.size(),1),w); |
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78 | |
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79 | alpha_ = m_y(); |
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80 | beta_ = sxy()/sxx(); |
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81 | |
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82 | chisq_=0; |
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83 | for (size_t i=0; i<x.size(); ++i){ |
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84 | double res = predict(x(i))-y(i); |
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85 | chisq_ += w(i)*res*res; |
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86 | } |
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87 | |
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88 | alpha_var_ = s2()/ap_.y_averager().sum_w(); |
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89 | beta_var_ = s2()/sxx(); |
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90 | } |
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91 | |
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92 | double LinearWeighted::m_x(void) const |
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93 | { |
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94 | return ap_.x_averager().mean(); |
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95 | } |
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96 | |
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97 | double LinearWeighted::m_y(void) const |
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98 | { |
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99 | return ap_.y_averager().mean(); |
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100 | } |
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101 | |
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102 | double LinearWeighted::predict(const double x) const |
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103 | { |
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104 | return alpha_ + beta_ * (x-m_x()); |
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105 | } |
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106 | |
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107 | |
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108 | double LinearWeighted::s2(double w) const |
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109 | { |
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110 | return chisq_/(w*(ap_.y_averager().n()-2)); |
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111 | } |
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112 | |
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113 | double LinearWeighted::standard_error2(const double x) const |
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114 | { |
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115 | return alpha_var_ + beta_var_*(x-m_x())*(x-m_x()); |
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116 | } |
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117 | |
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118 | |
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119 | double LinearWeighted::sxx(void) const |
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120 | { |
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121 | return ap_.x_averager().sum_xx_centered(); |
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122 | } |
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123 | |
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124 | |
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125 | double LinearWeighted::sxy(void) const |
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126 | { |
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127 | return ap_.sum_xy_centered(); |
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128 | } |
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129 | |
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130 | |
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131 | double LinearWeighted::syy(void) const |
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132 | { |
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133 | return ap_.y_averager().sum_xx_centered(); |
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134 | } |
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135 | |
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136 | }}} // of namespaces regression, yat, and theplu |
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