source: trunk/yat/regression/Polynomial.h @ 728

Last change on this file since 728 was 728, checked in by Peter, 16 years ago

added virtual function s2 in OneDimensional?.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.4 KB
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1#ifndef _theplu_yat_regression_polynomial_
2#define _theplu_yat_regression_polynomial_
3
4// $Id: Polynomial.h 728 2007-01-04 16:07:16Z peter $
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     @brief Polynomial regression
41     
42     Data are modeled as \f$ y = \alpha + \beta x + \gamma x^2 +
43     ... + \delta x_i^{\textrm{power}} + \epsilon_i \f$
44  */
45  class Polynomial : public OneDimensional
46  {
47  public:
48
49    ///
50    /// @param power degree of polynomial, e.g. 1 for a linear model
51    ///
52    explicit Polynomial(size_t power);
53
54    ///
55    /// @brief Destructor
56    ///
57    ~Polynomial(void);
58
59    ///
60    /// @brief covariance of parameters
61    ///
62    const utility::matrix& covariance(void) const;
63
64    ///
65    /// Fit the model by minimizing the mean squared deviation between
66    /// model and data.
67    ///
68    void fit(const utility::vector& x, const utility::vector& y);
69
70    ///
71    /// @return parameters of the model
72    ///
73    /// @see MultiDimensional
74    ///
75    const utility::vector& fit_parameters(void) const;
76
77    ///
78    /// @brief Sum of squared residuals
79    ///
80    double chisq(void) const;
81
82    ///
83    /// @return value in @a x of model
84    ///
85    double predict(const double x) const;
86
87    /**
88       \f$ \frac{\sum \epsilon_i^2}{N-\textrm{DF}} \f$
89       where DF is number of parameters in model.
90
91       @return variance of residuals
92    */
93    double s2(void) const;
94
95    ///
96    /// @return squared error of model value in @a x
97    ///
98    double standard_error2(const double x) const;
99
100  private:
101    MultiDimensional md_;
102    size_t power_;
103
104  };
105
106}}} // of namespaces regression, yat, and theplu
107
108#endif
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