# source:trunk/yat/regression/Polynomial.h@726

Last change on this file since 726 was 726, checked in by Peter, 16 years ago

fixes #165 added test checking Linear Regression is equivalent to Polynomial regression of degree one.

• 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// $Id: Polynomial.h 726 2007-01-04 14:38:56Z peter$
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
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    /// @return error of model value in @a x
89    ///
90    double standard_error(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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