1 | // $Id: MultiDimensionalWeighted.cc 703 2006-12-18 00:47:44Z 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 "MultiDimensionalWeighted.h" |
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25 | #include "yat/utility/matrix.h" |
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26 | #include "yat/utility/vector.h" |
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27 | |
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28 | #include <cassert> |
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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 | MultiDimensionalWeighted::MultiDimensionalWeighted(void) |
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35 | : chisquare_(0), work_(NULL) |
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36 | { |
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37 | } |
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38 | |
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39 | MultiDimensionalWeighted::~MultiDimensionalWeighted(void) |
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40 | { |
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41 | if (work_) |
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42 | gsl_multifit_linear_free(work_); |
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43 | } |
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44 | |
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45 | void MultiDimensionalWeighted::fit(const utility::matrix& x, |
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46 | const utility::vector& y, |
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47 | const utility::vector& w) |
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48 | { |
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49 | assert(y.size()==w.size()); |
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50 | assert(x.rows()==y.size()); |
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51 | |
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52 | covariance_=utility::matrix(x.columns(),x.columns()); |
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53 | fit_parameters_=utility::vector(x.columns()); |
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54 | if (work_) |
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55 | gsl_multifit_linear_free(work_); |
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56 | work_=gsl_multifit_linear_alloc(x.rows(),fit_parameters_.size()); |
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57 | gsl_multifit_wlinear(x.gsl_matrix_p(),w.gsl_vector_p(),y.gsl_vector_p(), |
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58 | fit_parameters_.gsl_vector_p(), |
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59 | covariance_.gsl_matrix_p(),&chisquare_,work_); |
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60 | } |
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61 | |
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62 | double MultiDimensionalWeighted::prediction_error(const utility::vector& x, |
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63 | const double w) const |
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64 | { |
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65 | double s2 = 0; |
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66 | for (size_t i=0; i<x.size(); ++i){ |
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67 | s2 += covariance_(i,i)*x(i)*x(i); |
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68 | for (size_t j=i+1; j<x.size(); ++j) |
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69 | s2 += 2*covariance_(i,j)*x(i)*x(j); |
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70 | } |
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71 | return sqrt(s2+chisquare_/w); |
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72 | } |
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73 | |
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74 | |
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75 | double MultiDimensionalWeighted::standard_error(const utility::vector& x) const |
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76 | { |
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77 | double s2 = 0; |
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78 | for (size_t i=0; i<x.size(); ++i){ |
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79 | s2 += covariance_(i,i)*x(i)*x(i); |
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80 | for (size_t j=i+1; j<x.size(); ++j) |
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81 | s2 += 2*covariance_(i,j)*x(i)*x(j); |
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82 | } |
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83 | return sqrt(s2); |
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84 | } |
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85 | |
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86 | }}} // of namespaces regression, yat, and theplu |
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