source: trunk/yat/regression/LinearWeighted.cc @ 702

Last change on this file since 702 was 702, checked in by Peter, 15 years ago

Refs #81 moved mse_ to inherited classes and made mse() pure virtual because mse is calculated different for different classes and therefore this design is more logic. Fixed docs and other things...

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 1.7 KB
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1// $Id: LinearWeighted.cc 702 2006-10-26 14:04:35Z 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 "LinearWeighted.h"
25#include "yat/statistics/AveragerPairWeighted.h"
26#include "yat/utility/vector.h"
27
28#include <gsl/gsl_fit.h>
29
30namespace theplu {
31namespace yat {
32namespace regression {
33
34  void LinearWeighted::fit(const utility::vector& x,
35                           const utility::vector& y,
36                           const utility::vector& w)
37  {
38    // AveragerPairWeighted requires 2 weights but works only on the
39    // product wx*wy, so we can send in w and a dummie to get what we
40    // want.
41    ap_.reset();
42    ap_.add_values(x,y,utility::vector(x.size(),1),w);
43
44    // estimating the noise level. see attached document for motivation
45    // of the expression.
46    s2_= (syy()-sxy()*sxy()/sxx())/(w.sum()-2*(w*w)/w.sum()) ;
47   
48    alpha_ = m_y();
49    beta_ = sxy()/sxx();
50    alpha_var_ = ap_.y_averager().standard_error() * 
51      ap_.y_averager().standard_error();
52    beta_var_ = s2_/sxx(); 
53    m_x_=m_x();
54  }
55
56}}} // of namespaces regression, yat, and theplu
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