source: trunk/c++_tools/statistics/MultiDimensionalWeighted.cc @ 675

Last change on this file since 675 was 675, checked in by Jari Häkkinen, 15 years ago

References #83. Changing project name to yat. Compilation will fail in this revision.

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1// $Id: MultiDimensionalWeighted.cc 675 2006-10-10 12:08:45Z jari $
2
3/*
4  Copyright (C) The authors contributing to this file.
5
6  This file is part of the yat library, http://lev.thep.lu.se/trac/yat
7
8  The yat library is free software; you can redistribute it and/or
9  modify it under the terms of the GNU General Public License as
10  published by the Free Software Foundation; either version 2 of the
11  License, or (at your option) any later version.
12
13  The yat library is distributed in the hope that it will be useful,
14  but WITHOUT ANY WARRANTY; without even the implied warranty of
15  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
16  General Public License for more details.
17
18  You should have received a copy of the GNU General Public License
19  along with this program; if not, write to the Free Software
20  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
21  02111-1307, USA.
22*/
23
24#include "yat/statistics/MultiDimensionalWeighted.h"
25#include "yat/utility/matrix.h"
26#include "yat/utility/vector.h"
27
28#include <cassert>
29
30namespace theplu {
31namespace statistics {
32namespace regression {
33
34
35  void MultiDimensionalWeighted::fit(const utility::matrix& x, 
36                                     const utility::vector& y,
37                                     const utility::vector& w)
38  {
39    assert(y.size()==w.size());
40    assert(x.rows()==y.size());
41
42    covariance_=utility::matrix(x.columns(),x.columns());
43    fit_parameters_=utility::vector(x.columns());
44    if (work_)
45      gsl_multifit_linear_free(work_);
46    work_=gsl_multifit_linear_alloc(x.rows(),fit_parameters_.size());
47    gsl_multifit_wlinear(x.gsl_matrix_p(),w.gsl_vector_p(),y.gsl_vector_p(),
48                         fit_parameters_.gsl_vector_p(),
49                         covariance_.gsl_matrix_p(),&chisquare_,work_);
50  }
51
52  double MultiDimensionalWeighted::prediction_error(const utility::vector& x,
53                                                    const double w) const
54  {
55    double s2 = 0;
56    for (size_t i=0; i<x.size(); ++i){
57      s2 += covariance_(i,i)*x(i)*x(i);
58      for (size_t j=i+1; j<x.size(); ++j)
59        s2 += 2*covariance_(i,j)*x(i)*x(j);
60    }
61    return sqrt(s2+chisquare_/w);
62  }
63
64
65  double MultiDimensionalWeighted::standard_error(const utility::vector& x) const
66  {
67    double s2 = 0;
68    for (size_t i=0; i<x.size(); ++i){
69      s2 += covariance_(i,i)*x(i)*x(i);
70      for (size_t j=i+1; j<x.size(); ++j)
71        s2 += 2*covariance_(i,j)*x(i)*x(j);
72    }
73    return sqrt(s2);
74  }
75
76}}} // of namespaces regression, statisitcs and thep
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