source: trunk/yat/regression/Linear.cc @ 703

Last change on this file since 703 was 703, checked in by Jari Häkkinen, 16 years ago

Addresses #65 and #170.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.0 KB
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1// $Id: Linear.cc 703 2006-12-18 00:47:44Z 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 "Linear.h"
25#include "yat/statistics/AveragerPair.h"
26#include "yat/utility/vector.h"
27
28#include <gsl/gsl_fit.h>
29
30namespace theplu {
31namespace yat {
32namespace regression {
33
34  Linear::Linear(void)
35    : OneDimensional(), alpha_(0), alpha_var_(0), beta_(0), beta_var_(0),
36      mse_(0), m_x_(0)
37  {
38  }
39
40  Linear::~Linear(void)
41  {
42  }
43         
44  void Linear::fit(const utility::vector& x, const utility::vector& y) 
45  {
46    ap_.reset();
47    for (size_t i=0; i<x.size(); i++)
48      ap_.add(x(i),y(i));
49
50    alpha_ = ap_.y_averager().mean();
51    beta_ = ap_.covariance() / ap_.x_averager().variance();
52
53    // calculating deviation between data and model
54    mse_ = (ap_.y_averager().sum_xx_centered() - ap_.sum_xy_centered() *
55            ap_.sum_xy_centered()/ap_.x_averager().sum_xx_centered() )/x.size();
56    r2_= 1-mse_/ap_.x_averager().variance();
57    alpha_var_ = mse_ / x.size();
58    beta_var_ = mse_ / ap_.x_averager().sum_xx_centered();
59    m_x_ = ap_.x_averager().mean();
60  }
61
62  double Linear::predict(const double x) const
63  { 
64    return alpha_ + beta_ * (x-m_x_); 
65  }
66
67  double Linear::standard_error(const double x) const
68  {
69    return sqrt( alpha_var_+beta_var_*(x-m_x_)*(x-m_x_)); 
70  }
71
72}}} // of namespaces regression, yat, and theplu
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