source: trunk/yat/regression/Polynomial.h @ 1437

Last change on this file since 1437 was 1437, checked in by Peter, 13 years ago

merge patch release 0.4.2 to trunk. Delta 0.4.2-0.4.1

  • 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 1437 2008-08-25 17:55:00Z peter $
5
6/*
7  Copyright (C) 2005, 2006, 2007 Jari Häkkinen, Peter Johansson
8  Copyright (C) 2008 Peter Johansson
9
10  This file is part of the yat library, http://dev.thep.lu.se/yat
11
12  The yat library is free software; you can redistribute it and/or
13  modify it under the terms of the GNU General Public License as
14  published by the Free Software Foundation; either version 2 of the
15  License, or (at your option) any later version.
16
17  The yat library is distributed in the hope that it will be useful,
18  but WITHOUT ANY WARRANTY; without even the implied warranty of
19  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
20  General Public License for more details.
21
22  You should have received a copy of the GNU General Public License
23  along with this program; if not, write to the Free Software
24  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
25  02111-1307, USA.
26*/
27
28#include "OneDimensional.h"
29#include "MultiDimensional.h"
30
31namespace theplu {
32namespace yat {
33namespace utility {
34  class VectorBase;
35}
36namespace regression {
37
38  /**
39     @brief Polynomial regression
40     
41     Data are modeled as \f$ y = \alpha + \beta x + \gamma x^2 +
42     ... + \delta x_i^{\textrm{power}} + \epsilon_i \f$
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    explicit Polynomial(size_t power);
52
53    ///
54    /// @brief Destructor
55    ///
56    ~Polynomial(void);
57
58    ///
59    /// @brief covariance of parameters
60    ///
61    const utility::Matrix& covariance(void) const;
62
63    ///
64    /// Fit the model by minimizing the mean squared deviation between
65    /// model and data.
66    ///
67    void fit(const utility::VectorBase& x, const utility::VectorBase& y);
68
69    ///
70    /// @return parameters of the model
71    ///
72    /// @see MultiDimensional
73    ///
74    const utility::Vector& fit_parameters(void) const;
75
76    ///
77    /// @return value in @a x of model
78    ///
79    double predict(const double x) const;
80
81    /**
82       \f$ \frac{\sum \epsilon_i^2}{N-\textrm{DF}} \f$
83       where DF is number of parameters in model.
84
85       @return variance of residuals
86    */
87    double s2(void) const;
88
89    ///
90    /// @return squared error of model value in @a x
91    ///
92    double standard_error2(const double x) const;
93
94  private:
95    MultiDimensional md_;
96    size_t power_;
97
98  };
99
100}}} // of namespaces regression, yat, and theplu
101
102#endif
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