source: trunk/yat/regression/MultiDimensional.cc

Last change on this file was 4207, checked in by Peter, 5 weeks ago

update copyright statements

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1// $Id: MultiDimensional.cc 4207 2022-08-26 04:36:28Z peter $
2
3/*
4  Copyright (C) 2005 Jari Häkkinen
5  Copyright (C) 2006, 2007, 2008 Jari Häkkinen, Peter Johansson
6  Copyright (C) 2011, 2012, 2020, 2022 Peter Johansson
7
8  This file is part of the yat library, http://dev.thep.lu.se/yat
9
10  The yat library is free software; you can redistribute it and/or
11  modify it under the terms of the GNU General Public License as
12  published by the Free Software Foundation; either version 3 of the
13  License, or (at your option) any later version.
14
15  The yat library is distributed in the hope that it will be useful,
16  but WITHOUT ANY WARRANTY; without even the implied warranty of
17  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18  General Public License for more details.
19
20  You should have received a copy of the GNU General Public License
21  along with yat. If not, see <http://www.gnu.org/licenses/>.
22*/
23
24#include <config.h>
25
26#include "MultiDimensional.h"
27#include "yat/utility/Exception.h"
28#include "yat/utility/Matrix.h"
29#include "yat/utility/VectorBase.h"
30#include "yat/utility/Vector.h"
31
32#include <cassert>
33
34namespace theplu {
35namespace yat {
36namespace regression {
37
38
39  MultiDimensional::MultiDimensional(void)
40    : chisquare_(0), work_(NULL)
41  {
42  }
43
44
45  MultiDimensional::~MultiDimensional(void)
46  {
47    gsl_multifit_linear_free(work_);
48  }
49
50
51  const utility::Matrix& MultiDimensional::covariance(void) const
52  {
53    return covariance_;
54  }
55
56
57  void MultiDimensional::fit(const utility::Matrix& x,
58                             const utility::VectorBase& y)
59  {
60    fit2(x, y);
61  }
62
63
64  void MultiDimensional::fit2(const utility::MatrixBase& x,
65                              const utility::VectorBase& y)
66  {
67    assert(x.rows()==y.size());
68    covariance_.resize(x.columns(),x.columns());
69    fit_parameters_.resize(x.columns());
70    gsl_multifit_linear_free(work_);
71    if (!(work_=gsl_multifit_linear_alloc(x.rows(),fit_parameters_.size())))
72      throw utility::GSL_error("MultiDimensional::fit failed to allocate memory");
73
74    int status = gsl_multifit_linear(x.gsl_matrix_p(), y.gsl_vector_p(),
75                                     fit_parameters_.gsl_vector_p(),
76                                     covariance_.gsl_matrix_p(), &chisquare_,
77                                     work_);
78    if (status)
79      throw utility::GSL_error(std::string("MultiDimensional::fit",status));
80
81    s2_ = chisquare_/(x.rows()-x.columns());
82  }
83
84
85  const utility::Vector& MultiDimensional::fit_parameters(void) const
86  {
87    return fit_parameters_;
88  }
89
90
91  double MultiDimensional::chisq(void) const
92  {
93    return chisquare_;
94  }
95
96
97  double MultiDimensional::predict(const utility::VectorBase& x) const
98  {
99    assert(x.size()==fit_parameters_.size());
100    return fit_parameters_ * x;
101  }
102
103
104  double MultiDimensional::prediction_error2(const utility::VectorBase& x) const
105  {
106    return standard_error2(x) + s2_;
107  }
108
109
110  double MultiDimensional::standard_error2(const utility::VectorBase& x) const
111  {
112    double s2 = 0;
113    for (size_t i=0; i<x.size(); ++i){
114      s2 += covariance_(i,i)*x(i)*x(i);
115      for (size_t j=i+1; j<x.size(); ++j)
116        s2 += 2*covariance_(i,j)*x(i)*x(j);
117    }
118    return s2;
119  }
120
121}}} // of namespaces regression, yat, and theplu
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