source: trunk/yat/regression/PolynomialWeighted.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: 2.3 KB
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1#ifndef _theplu_yat_regression_polynomialweighted_
2#define _theplu_yat_regression_polynomialweighted_
3
4// $Id: PolynomialWeighted.h 1437 2008-08-25 17:55:00Z peter $
5
6/*
7  Copyright (C) 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 "OneDimensionalWeighted.h"
29#include "MultiDimensionalWeighted.h"
30#include "yat/utility/Vector.h"
31
32namespace theplu {
33namespace yat {
34namespace regression {
35
36  ///
37  /// @brief Polynomial Regression in weighted fashion.
38  ///
39  class PolynomialWeighted : public OneDimensionalWeighted
40  {
41  public:
42
43    ///
44    /// @param power degree of polynomial model
45    ///
46    PolynomialWeighted(size_t power);
47
48    ///
49    /// @brief Destructor
50    ///
51    ~PolynomialWeighted(void);
52
53    ///
54    /// This function computes the best-fit given the polynomial model
55    /// model by minimizing \f$ \sum{w_i(\hat{y_i}-y_i)^2} \f$, where
56    /// \f$ \hat{y} \f$ is the fitted value. The weight \f$ w_i \f$
57    /// should be proportional to the inverse of the variance for \f$
58    /// y_i \f$
59    ///
60    void fit(const utility::VectorBase& x, const utility::VectorBase& y,
61             const utility::VectorBase& w);
62
63    ///
64    /// @return parameters of the model
65    ///
66    /// @see MultiDimensional
67    ///
68    const utility::Vector& fit_parameters(void) const;
69
70    ///
71    /// @brief Mean Squared Error
72    ///
73    double s2(const double w=1) const;
74
75    ///
76    /// function predicting in one point.
77    ///
78    double predict(const double x) const;
79
80    ///
81    /// @return error of model value in @a x
82    ///
83    double standard_error2(const double x) const;
84
85  private:
86    MultiDimensionalWeighted md_;
87    size_t power_;
88
89  };
90
91}}} // of namespaces regression, yat, and theplu
92
93#endif
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