source: trunk/yat/regression/MultiDimensional.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: 2.5 KB
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1// $Id: MultiDimensional.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 "MultiDimensional.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
35  MultiDimensional::MultiDimensional(void)
36    : chisquare_(0), work_(NULL)
37  {
38  }
39
40
41  MultiDimensional::~MultiDimensional(void)
42  {
43    if (work_)
44      gsl_multifit_linear_free(work_);
45  }
46
47
48  const utility::matrix& MultiDimensional::covariance(void) const
49  {
50    return covariance_;
51  }
52
53
54  void MultiDimensional::fit(const utility::matrix& x, const utility::vector& y)
55  {
56    assert(x.rows()==y.size());
57    covariance_=utility::matrix(x.columns(),x.columns());
58    fit_parameters_=utility::vector(x.columns());
59    if (work_)
60      gsl_multifit_linear_free(work_);
61    work_=gsl_multifit_linear_alloc(x.rows(),fit_parameters_.size());
62    gsl_multifit_linear(x.gsl_matrix_p(),y.gsl_vector_p(),
63                        fit_parameters_.gsl_vector_p(),
64                        covariance_.gsl_matrix_p(),&chisquare_,work_);
65  }
66
67  const utility::vector& MultiDimensional::fit_parameters(void) const
68  { 
69    return fit_parameters_; 
70  }
71
72
73  double MultiDimensional::chisq(void) const
74  {
75    return chisquare_;
76  }
77
78
79  double MultiDimensional::predict(const utility::vector& x) const 
80  {
81    assert(x.size()==fit_parameters_.size());
82    return fit_parameters_ * x;
83  }
84
85
86  double MultiDimensional::prediction_error2(const utility::vector& x) const
87  {
88    return standard_error2(x)+chisquare_;
89  }
90
91
92  double MultiDimensional::standard_error2(const utility::vector& x) const
93  {
94    double s2 = 0;
95    for (size_t i=0; i<x.size(); ++i){
96      s2 += covariance_(i,i)*x(i)*x(i);
97      for (size_t j=i+1; j<x.size(); ++j)
98        s2 += 2*covariance_(i,j)*x(i)*x(j);
99    }
100    return s2;
101  }
102
103}}} // of namespaces regression, yat, and theplu
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