source: trunk/yat/regression/OneDimensional.cc @ 1486

Last change on this file since 1486 was 1486, checked in by Jari Häkkinen, 13 years ago

Addresses #436.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 1.9 KB
Line 
1// $Id: OneDimensional.cc 1486 2008-09-09 21:17:19Z jari $
2
3/*
4  Copyright (C) 2005 Peter Johansson
5  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson
6  Copyright (C) 2008 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 this program; if not, write to the Free Software
22  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
23  02111-1307, USA.
24*/
25
26#include "OneDimensional.h"
27
28namespace theplu {
29namespace yat {
30namespace regression {
31
32  OneDimensional::OneDimensional(void)
33    : chisq_(0)
34  {
35  }
36
37  OneDimensional::~OneDimensional(void)
38  {
39  }
40
41
42  double OneDimensional::chisq(void) const
43  {
44    return chisq_;
45  }
46
47
48  double OneDimensional::prediction_error2(const double x) const 
49  { 
50    return s2()+standard_error2(x); 
51  }
52
53
54  std::ostream& OneDimensional::print(std::ostream& os, const double min, 
55                                      double max, const unsigned int n) const
56  {
57    double dx;
58    if (n>1)
59      dx=(max-min)/(n-1);
60    else{
61      dx=1.0;
62      max=min;
63    }
64
65    for ( double x=min; x<=max; x+=dx) {
66      double y = predict(x);
67      double y_err = sqrt(prediction_error2(x));
68      os << x << "\t" << y << "\t" << y_err << "\n";
69    }
70    return os;
71  }
72
73
74  double OneDimensional::r2(void) const
75  {
76    return 1 - chisq()/ap_.y_averager().sum_xx_centered();
77  }
78
79  double OneDimensional::variance(void) const
80  {
81    return ap_.y_averager().variance();
82  }
83
84}}} // of namespaces regression, yat, and theplu
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