1 | #ifndef _theplu_yat_regression_onedimensional_ |
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2 | #define _theplu_yat_regression_onedimensional_ |
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3 | |
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4 | // $Id: OneDimensional.h 1486 2008-09-09 21:17:19Z jari $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004 Peter Johansson |
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8 | Copyright (C) 2005, 2006, 2007 Jari Häkkinen, Peter Johansson |
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9 | Copyright (C) 2008 Peter Johansson |
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10 | |
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11 | This file is part of the yat library, http://dev.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 3 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/statistics/AveragerPair.h" |
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30 | |
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31 | #include <ostream> |
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32 | |
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33 | namespace theplu { |
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34 | namespace yat { |
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35 | namespace utility { |
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36 | class VectorBase; |
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37 | } |
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38 | namespace regression { |
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39 | |
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40 | /// |
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41 | /// @brief Interface Class for One Dimensional fitting. |
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42 | /// |
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43 | /// @see OneDimensionalWeighted. |
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44 | /// |
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45 | class OneDimensional |
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46 | { |
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47 | |
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48 | public: |
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49 | /// |
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50 | /// @brief The default constructor |
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51 | /// |
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52 | OneDimensional(void); |
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53 | |
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54 | /// |
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55 | /// @brief The destructor |
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56 | /// |
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57 | virtual ~OneDimensional(void); |
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58 | |
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59 | /** |
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60 | @brief Chi-squared |
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61 | |
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62 | Chi-squared is defined as the \f$ |
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63 | \sum{(\hat{y_i}-y_i)^2} \f$ |
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64 | */ |
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65 | double chisq(void) const; |
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66 | |
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67 | /** |
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68 | This function computes the best-fit given a model (see specific |
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69 | class for details) by minimizing \f$ \sum{(\hat{y_i}-y_i)^2} |
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70 | \f$, where \f$ \hat{y} \f$ is the fitted value. |
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71 | */ |
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72 | virtual void fit(const utility::VectorBase& x, |
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73 | const utility::VectorBase& y)=0; |
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74 | |
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75 | /// |
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76 | /// @return expected value in @a x accrding to the fitted model |
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77 | /// |
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78 | virtual double predict(const double x) const=0; |
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79 | |
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80 | /** |
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81 | The prediction error is defined as the expected squared |
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82 | deviation a new data point will have from value the model |
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83 | provides: \f$ E(Y|x - \hat{y}(x))^2 \f$ and is typically |
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84 | divided into the conditional variance ( see s2() ) |
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85 | given \f$ x \f$ and the squared standard error ( see |
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86 | standard_error2() ) of the model estimation in \f$ x \f$. |
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87 | |
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88 | @return expected squared prediction error for a new data point |
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89 | in @a x |
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90 | */ |
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91 | double prediction_error2(const double x) const; |
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92 | |
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93 | /// |
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94 | /// @brief print output to ostream @a os |
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95 | /// |
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96 | /// Printing estimated model to @a os in the points defined by @a |
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97 | /// min, @a max, and @a n. The values printed for each point is |
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98 | /// the x-value, the estimated y-value, and the estimated standard |
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99 | /// deviation of a new data poiunt will have from the y-value |
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100 | /// given the x-value (see prediction_error()). |
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101 | /// |
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102 | /// @param os Ostream printout is sent to |
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103 | /// @param n number of points printed |
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104 | /// @param min smallest x-value for which the model is printed |
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105 | /// @param max largest x-value for which the model is printed |
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106 | /// |
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107 | std::ostream& print(std::ostream& os,const double min, |
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108 | double max, const unsigned int n) const; |
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109 | |
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110 | /** |
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111 | r2 is defined as \f$ 1 - \frac{Var(Y|x)}{Var(Y)} \f$ or the |
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112 | fraction of the variance explained by the regression model. |
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113 | |
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114 | @see s2() |
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115 | */ |
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116 | double r2(void) const; |
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117 | |
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118 | /** |
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119 | \f$ E(Y|x - E(Y|x))^2 \f$ |
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120 | |
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121 | @return Conditional variance of Y |
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122 | */ |
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123 | virtual double s2(void) const=0; |
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124 | |
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125 | /** |
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126 | The standard error is defined as \f$ E(Y|x - \hat{y}(x))^2 \f$ |
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127 | |
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128 | @return expected squared error of model value in @a x |
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129 | */ |
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130 | virtual double standard_error2(const double x) const=0; |
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131 | |
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132 | protected: |
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133 | /// |
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134 | /// Variance of y |
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135 | /// |
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136 | double variance(void) const; |
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137 | |
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138 | /// |
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139 | /// Averager for pair of x and y |
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140 | /// |
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141 | statistics::AveragerPair ap_; |
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142 | |
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143 | /// |
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144 | /// @see chisq() |
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145 | /// |
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146 | double chisq_; |
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147 | }; |
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148 | |
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149 | }}} // of namespaces regression, yat, and theplu |
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150 | |
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151 | #endif |
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