source: trunk/yat/regression/OneDimensional.cc @ 1679

Last change on this file since 1679 was 1487, checked in by Jari Häkkinen, 13 years ago

Addresses #436. GPL license copy reference should also be updated.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 1.8 KB
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1// $Id: OneDimensional.cc 1487 2008-09-10 08:41:36Z 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 yat. If not, see <http://www.gnu.org/licenses/>.
22*/
23
24#include "OneDimensional.h"
25
26namespace theplu {
27namespace yat {
28namespace regression {
29
30  OneDimensional::OneDimensional(void)
31    : chisq_(0)
32  {
33  }
34
35  OneDimensional::~OneDimensional(void)
36  {
37  }
38
39
40  double OneDimensional::chisq(void) const
41  {
42    return chisq_;
43  }
44
45
46  double OneDimensional::prediction_error2(const double x) const 
47  { 
48    return s2()+standard_error2(x); 
49  }
50
51
52  std::ostream& OneDimensional::print(std::ostream& os, const double min, 
53                                      double max, const unsigned int n) const
54  {
55    double dx;
56    if (n>1)
57      dx=(max-min)/(n-1);
58    else{
59      dx=1.0;
60      max=min;
61    }
62
63    for ( double x=min; x<=max; x+=dx) {
64      double y = predict(x);
65      double y_err = sqrt(prediction_error2(x));
66      os << x << "\t" << y << "\t" << y_err << "\n";
67    }
68    return os;
69  }
70
71
72  double OneDimensional::r2(void) const
73  {
74    return 1 - chisq()/ap_.y_averager().sum_xx_centered();
75  }
76
77  double OneDimensional::variance(void) const
78  {
79    return ap_.y_averager().variance();
80  }
81
82}}} // of namespaces regression, yat, and theplu
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