source: trunk/yat/regression/Linear.cc @ 1019

Last change on this file since 1019 was 1019, checked in by Peter, 15 years ago

passing VectorBase? in regression::OneDimesional? - refs #256

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1// $Id: Linear.cc 1019 2008-02-01 17:10:34Z peter $
2
3/*
4  Copyright (C) 2004, 2005, 2006, 2007 Jari Häkkinen, Peter Johansson
5
6  This file is part of the yat library, http://trac.thep.lu.se/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/VectorBase.h"
27
28namespace theplu {
29namespace yat {
30namespace regression {
31
32  Linear::Linear(void)
33    : OneDimensional(), alpha_(0), alpha_var_(0), beta_(0), beta_var_(0)
34  {
35  }
36
37  Linear::~Linear(void)
38  {
39  }
40
41  double Linear::alpha(void) const
42  {
43    return alpha_;
44  }
45
46  double Linear::alpha_var(void) const
47  {
48    return alpha_var_;
49  }
50
51  double Linear::beta(void) const
52  {
53    return beta_;
54  }
55
56  double Linear::beta_var(void) const
57  {
58    return beta_var_;
59  }
60
61  void Linear::fit(const utility::VectorBase& x, const utility::VectorBase& y) 
62  {
63    ap_.reset();
64    for (size_t i=0; i<x.size(); i++)
65      ap_.add(x(i),y(i));
66
67    alpha_ = ap_.y_averager().mean();
68    beta_ = ap_.sum_xy_centered() / ap_.x_averager().sum_xx_centered();
69
70    // calculating deviation between data and model
71    chisq_ = (ap_.y_averager().sum_xx_centered() - ap_.sum_xy_centered()*
72              ap_.sum_xy_centered()/ap_.x_averager().sum_xx_centered() );
73    alpha_var_ = s2() / x.size();
74    beta_var_ = s2() / ap_.x_averager().sum_xx_centered();
75  }
76
77  double Linear::predict(const double x) const
78  { 
79    return alpha_ + beta_ * (x - ap_.x_averager().mean()); 
80  }
81
82  double Linear::s2(void) const
83  {
84    return chisq()/(ap_.n()-2);
85  }
86
87  double Linear::standard_error2(const double x) const
88  {
89    return alpha_var_+beta_var_*(x-ap_.x_averager().mean())*
90      (x-ap_.x_averager().mean()); 
91  }
92
93}}} // of namespaces regression, yat, and theplu
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