1 | // $Id: LinearWeighted.cc 718 2006-12-26 09:56:26Z jari $ |
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2 | |
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3 | /* |
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4 | Copyright (C) The authors contributing to this file. |
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5 | |
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6 | This file is part of the yat library, http://lev.thep.lu.se/trac/yat |
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7 | |
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8 | The yat library is free software; you can redistribute it and/or |
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9 | modify it under the terms of the GNU General Public License as |
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10 | published by the Free Software Foundation; either version 2 of the |
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11 | License, or (at your option) any later version. |
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12 | |
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13 | The yat library is distributed in the hope that it will be useful, |
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14 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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16 | General Public License for more details. |
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17 | |
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18 | You should have received a copy of the GNU General Public License |
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19 | along with this program; if not, write to the Free Software |
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20 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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21 | 02111-1307, USA. |
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22 | */ |
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23 | |
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24 | #include "LinearWeighted.h" |
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25 | #include "yat/statistics/AveragerPairWeighted.h" |
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26 | #include "yat/utility/vector.h" |
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27 | |
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28 | #include <gsl/gsl_fit.h> |
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29 | |
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30 | namespace theplu { |
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31 | namespace yat { |
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32 | namespace regression { |
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33 | |
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34 | LinearWeighted::LinearWeighted(void) |
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35 | : OneDimensionalWeighted(), alpha_(0), alpha_var_(0), beta_(0), |
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36 | beta_var_(0), m_x_(0), s2_(0) |
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37 | { |
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38 | } |
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39 | |
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40 | LinearWeighted::~LinearWeighted(void) |
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41 | { |
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42 | } |
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43 | |
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44 | double LinearWeighted::alpha(void) const |
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45 | { |
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46 | return alpha_; |
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47 | } |
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48 | |
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49 | double LinearWeighted::alpha_err(void) const |
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50 | { |
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51 | return sqrt(alpha_var_); |
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52 | } |
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53 | |
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54 | double LinearWeighted::beta(void) const |
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55 | { |
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56 | return beta_; |
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57 | } |
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58 | |
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59 | double LinearWeighted::beta_err(void) const |
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60 | { |
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61 | return sqrt(beta_var_); |
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62 | } |
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63 | |
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64 | void LinearWeighted::fit(const utility::vector& x, |
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65 | const utility::vector& y, |
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66 | const utility::vector& w) |
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67 | { |
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68 | // AveragerPairWeighted requires 2 weights but works only on the |
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69 | // product wx*wy, so we can send in w and a dummie to get what we |
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70 | // want. |
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71 | ap_.reset(); |
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72 | ap_.add_values(x,y,utility::vector(x.size(),1),w); |
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73 | |
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74 | // estimating the noise level. see attached document for motivation |
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75 | // of the expression. |
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76 | s2_= (syy()-sxy()*sxy()/sxx())/(w.sum()-2*(w*w)/w.sum()) ; |
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77 | |
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78 | alpha_ = m_y(); |
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79 | beta_ = sxy()/sxx(); |
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80 | alpha_var_ = ap_.y_averager().standard_error() * |
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81 | ap_.y_averager().standard_error(); |
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82 | beta_var_ = s2_/sxx(); |
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83 | m_x_=m_x(); |
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84 | } |
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85 | |
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86 | double LinearWeighted::m_x(void) const |
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87 | { |
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88 | return ap_.x_averager().mean(); |
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89 | } |
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90 | |
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91 | double LinearWeighted::m_y(void) const |
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92 | { |
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93 | return ap_.y_averager().mean(); |
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94 | } |
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95 | |
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96 | double LinearWeighted::mse(void) const |
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97 | { |
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98 | return mse_; |
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99 | } |
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100 | |
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101 | double LinearWeighted::prediction_error(const double x, const double w) const |
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102 | { |
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103 | return sqrt(alpha_var_ + beta_var_*(x-m_x_)*(x-m_x_)+s2(w)); |
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104 | } |
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105 | |
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106 | double LinearWeighted::s2(double w) const |
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107 | { |
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108 | return s2_/w; |
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109 | } |
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110 | |
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111 | double LinearWeighted::standard_error(const double x) const |
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112 | { |
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113 | return sqrt(alpha_var_ + beta_var_*(x-m_x_)*(x-m_x_) ); |
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114 | } |
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115 | |
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116 | double LinearWeighted::sxx(void) const |
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117 | { |
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118 | return ap_.x_averager().sum_xx_centered(); |
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119 | } |
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120 | |
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121 | double LinearWeighted::sxy(void) const |
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122 | { |
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123 | return ap_.sum_xy_centered(); |
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124 | } |
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125 | |
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126 | double LinearWeighted::syy(void) const |
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127 | { |
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128 | return ap_.y_averager().sum_xx_centered(); |
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129 | } |
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130 | |
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131 | }}} // of namespaces regression, yat, and theplu |
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