source: trunk/yat/regression/Polynomial.h @ 1703

Last change on this file since 1703 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: 2.3 KB
Line 
1#ifndef _theplu_yat_regression_polynomial_
2#define _theplu_yat_regression_polynomial_
3
4// $Id: Polynomial.h 1487 2008-09-10 08:41:36Z jari $
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 3 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 yat. If not, see <http://www.gnu.org/licenses/>.
24*/
25
26#include "OneDimensional.h"
27#include "MultiDimensional.h"
28
29namespace theplu {
30namespace yat {
31namespace utility {
32  class VectorBase;
33}
34namespace regression {
35
36  /**
37     @brief Polynomial regression
38     
39     Data are modeled as \f$ y = \alpha + \beta x + \gamma x^2 +
40     ... + \delta x_i^{\textrm{power}} + \epsilon_i \f$
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    /// @brief covariance of parameters
58    ///
59    const utility::Matrix& covariance(void) const;
60
61    ///
62    /// Fit the model by minimizing the mean squared deviation between
63    /// model and data.
64    ///
65    void fit(const utility::VectorBase& x, const utility::VectorBase& y);
66
67    ///
68    /// @return parameters of the model
69    ///
70    /// @see MultiDimensional
71    ///
72    const utility::Vector& fit_parameters(void) const;
73
74    ///
75    /// @return value in @a x of model
76    ///
77    double predict(const double x) const;
78
79    /**
80       \f$ \frac{\sum \epsilon_i^2}{N-\textrm{DF}} \f$
81       where DF is number of parameters in model.
82
83       @return variance of residuals
84    */
85    double s2(void) const;
86
87    ///
88    /// @return squared error of model value in @a x
89    ///
90    double standard_error2(const double x) const;
91
92  private:
93    MultiDimensional md_;
94    size_t power_;
95
96  };
97
98}}} // of namespaces regression, yat, and theplu
99
100#endif
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