source: trunk/c++_tools/statistics/Polynomial.h @ 675

Last change on this file since 675 was 675, checked in by Jari Häkkinen, 15 years ago

References #83. Changing project name to yat. Compilation will fail in this revision.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.2 KB
Line 
1#ifndef _theplu_statistics_regression_polynomial_
2#define _theplu_statistics_regression_polynomial_
3
4// $Id: Polynomial.h 675 2006-10-10 12:08:45Z jari $
5
6/*
7  Copyright (C) The authors contributing to this file.
8
9  This file is part of the yat library, http://lev.thep.lu.se/trac/yat
10
11  The yat library is free software; you can redistribute it and/or
12  modify it under the terms of the GNU General Public License as
13  published by the Free Software Foundation; either version 2 of the
14  License, or (at your option) any later version.
15
16  The yat library is distributed in the hope that it will be useful,
17  but WITHOUT ANY WARRANTY; without even the implied warranty of
18  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
19  General Public License for more details.
20
21  You should have received a copy of the GNU General Public License
22  along with this program; if not, write to the Free Software
23  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
24  02111-1307, USA.
25*/
26
27#include "yat/statistics/OneDimensional.h"
28#include "yat/statistics/MultiDimensional.h"
29
30#include "yat/utility/vector.h"
31
32#include <gsl/gsl_multifit.h>
33
34#include <cassert>
35
36
37namespace theplu {
38namespace statistics {
39namespace regression {
40
41  ///
42  /// @todo document
43  ///
44  class Polynomial : public OneDimensional
45  {
46  public:
47
48    ///
49    /// @param power degree of polynomial, e.g. 1 for a linear model
50    ///
51    inline Polynomial(size_t power)
52      : OneDimensional(), power_(power) {}
53
54    ///
55    /// @brief Destructor
56    ///
57    inline ~Polynomial(void) {};
58
59    ///
60    /// fit the model by minimizing the mean squared deviation between
61    /// model and data.
62    ///
63    void fit(const utility::vector& x, const utility::vector& y);
64
65    ///
66    /// @return parameters of the model
67    ///
68    utility::vector fit_parameters(void) { return md_.fit_parameters(); }
69
70    ///
71    /// @return value in @a x of model
72    ///
73    double predict(const double x) const;
74
75    ///
76    /// @return expected prediction error for a new data point in @a x
77    ///
78    double prediction_error(const double x) const; 
79
80    ///
81    /// @return error of model value in @a x
82    ///
83    double standard_error(const double x) const;
84
85  private:
86    MultiDimensional md_;
87    size_t power_;
88
89  };
90
91
92}}} // of namespaces regression, statisitcs and thep
93
94#endif
Note: See TracBrowser for help on using the repository browser.