1 | #ifndef _theplu_statistics_regression_onedimensioanlweighted_ |
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2 | #define _theplu_statistics_regression_onedimensioanlweighted_ |
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3 | |
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4 | // $Id: OneDimensionalWeighted.h 675 2006-10-10 12:08:45Z 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 <ostream> |
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28 | |
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29 | namespace theplu { |
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30 | namespace utility { |
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31 | class vector; |
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32 | } |
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33 | |
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34 | namespace statistics { |
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35 | namespace regression { |
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36 | |
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37 | /// |
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38 | /// Abstract Base Class for One Dimensional fitting in a weighted |
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39 | /// fashion. |
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40 | /// |
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41 | /// @todo document |
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42 | /// |
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43 | class OneDimensionalWeighted |
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44 | { |
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45 | |
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46 | public: |
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47 | /// |
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48 | /// Default Constructor. |
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49 | /// |
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50 | inline OneDimensionalWeighted(void):s2_(0) {} |
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51 | |
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52 | /// |
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53 | /// Destructor |
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54 | /// |
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55 | virtual ~OneDimensionalWeighted(void) {}; |
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56 | |
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57 | /// |
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58 | /// This function computes the best-fit given a model (see |
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59 | /// specific class for details) by minimizing \f$ |
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60 | /// \sum{w_i(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the |
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61 | /// fitted value. The weight \f$ w_i \f$ should be proportional |
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62 | /// to the inverse of the variance for \f$ y_i \f$ |
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63 | /// |
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64 | virtual void fit(const utility::vector& x, const utility::vector& y, |
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65 | const utility::vector& w)=0; |
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66 | |
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67 | /// |
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68 | /// function predicting in one point. |
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69 | /// |
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70 | virtual double predict(const double x) const=0; |
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71 | |
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72 | /// |
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73 | /// @return expected prediction error for a new data point in @a x |
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74 | /// with weight @a w |
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75 | /// |
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76 | virtual double prediction_error(const double x, const double w=1) const=0; |
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77 | |
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78 | /// |
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79 | /// @return error of model value in @a x |
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80 | /// |
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81 | virtual double standard_error(const double x) const=0; |
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82 | |
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83 | protected: |
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84 | /// |
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85 | /// noise level - the typical variance for a point with weight w |
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86 | /// is s2/w |
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87 | /// |
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88 | double s2_; |
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89 | }; |
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90 | |
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91 | }}} // of namespaces regression, statisitcs and thep |
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92 | |
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93 | #endif |
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