source: trunk/yat/regression/Polynomial.h @ 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 Id
File size: 2.1 KB
Line 
1#ifndef _theplu_yat_regression_polynomial_
2#define _theplu_yat_regression_polynomial_
3
4// $Id: Polynomial.h 703 2006-12-18 00:47:44Z 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 "OneDimensional.h"
28#include "MultiDimensional.h"
29#include "yat/utility/vector.h"
30
31#include <gsl/gsl_multifit.h>
32
33#include <cassert>
34
35namespace theplu {
36namespace yat {
37namespace regression {
38
39  ///
40  /// @todo document
41  ///
42  class Polynomial : public OneDimensional
43  {
44  public:
45
46    ///
47    /// @param power degree of polynomial, e.g. 1 for a linear model
48    ///
49    explicit Polynomial(size_t power);
50
51    ///
52    /// @brief Destructor
53    ///
54    ~Polynomial(void);
55
56    ///
57    /// fit the model by minimizing the mean squared deviation between
58    /// model and data.
59    ///
60    void fit(const utility::vector& x, const utility::vector& y);
61
62    ///
63    /// @return parameters of the model
64    ///
65    utility::vector fit_parameters(void) { return md_.fit_parameters(); }
66
67    ///
68    /// @todo
69    /// @brief Mean Squared Error
70    ///
71    inline double mse(void) const { return mse_; }
72
73    ///
74    /// @return value in @a x of model
75    ///
76    double predict(const double x) const;
77
78    ///
79    /// @return error of model value in @a x
80    ///
81    double standard_error(const double x) const;
82
83  private:
84    MultiDimensional md_;
85    double mse_;
86    size_t power_;
87
88  };
89
90}}} // of namespaces regression, yat, and theplu
91
92#endif
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