source: trunk/yat/regression/MultiDimensionalWeighted.cc @ 731

Last change on this file since 731 was 731, checked in by Peter, 16 years ago

added test for multidimensional weighted and straight version

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.6 KB
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1// $Id: MultiDimensionalWeighted.cc 731 2007-01-06 16:06:19Z peter $
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 "MultiDimensionalWeighted.h"
25#include "yat/utility/matrix.h"
26#include "yat/utility/vector.h"
27
28#include <cassert>
29
30namespace theplu {
31namespace yat {
32namespace regression {
33
34  MultiDimensionalWeighted::MultiDimensionalWeighted(void)
35    : chisquare_(0), work_(NULL)
36  {
37  }
38
39  MultiDimensionalWeighted::~MultiDimensionalWeighted(void)
40  {
41    if (work_)
42      gsl_multifit_linear_free(work_);
43  }
44
45  void MultiDimensionalWeighted::fit(const utility::matrix& x, 
46                                     const utility::vector& y,
47                                     const utility::vector& w)
48  {
49    assert(y.size()==w.size());
50    assert(x.rows()==y.size());
51
52    covariance_=utility::matrix(x.columns(),x.columns());
53    fit_parameters_=utility::vector(x.columns());
54    if (work_)
55      gsl_multifit_linear_free(work_);
56    work_=gsl_multifit_linear_alloc(x.rows(),fit_parameters_.size());
57    gsl_multifit_wlinear(x.gsl_matrix_p(),w.gsl_vector_p(),y.gsl_vector_p(),
58                         fit_parameters_.gsl_vector_p(),
59                         covariance_.gsl_matrix_p(),&chisquare_,work_);
60  }
61
62  double MultiDimensionalWeighted::predict(const utility::vector& x) const
63  {
64    assert(x.size()==fit_parameters_.size());
65    return fit_parameters_ * x;
66  }
67
68  double MultiDimensionalWeighted::prediction_error(const utility::vector& x,
69                                                    const double w) const
70  {
71    double s2 = 0;
72    for (size_t i=0; i<x.size(); ++i){
73      s2 += covariance_(i,i)*x(i)*x(i);
74      for (size_t j=i+1; j<x.size(); ++j)
75        s2 += 2*covariance_(i,j)*x(i)*x(j);
76    }
77    return sqrt(s2+chisquare_/w);
78  }
79
80
81  double 
82  MultiDimensionalWeighted::standard_error2(const utility::vector& x) const
83  {
84    double s2 = 0;
85    for (size_t i=0; i<x.size(); ++i){
86      s2 += covariance_(i,i)*x(i)*x(i);
87      for (size_t j=i+1; j<x.size(); ++j)
88        s2 += 2*covariance_(i,j)*x(i)*x(j);
89    }
90    return s2;
91  }
92
93}}} // of namespaces regression, yat, and theplu
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