source: trunk/c++_tools/statistics/PolynomialWeighted.h @ 675

Last change on this file since 675 was 675, checked in by Jari Häkkinen, 15 years ago

References #83. Changing project name to yat. Compilation will fail in this revision.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.5 KB
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1#ifndef _theplu_statistics_regression_polynomial_weighted_
2#define _theplu_statistics_regression_polynomial_weighted_
3
4// $Id: PolynomialWeighted.h 675 2006-10-10 12:08:45Z jari $
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 "yat/statistics/OneDimensionalWeighted.h"
28#include "yat/statistics/MultiDimensionalWeighted.h"
29
30#include "yat/utility/vector.h"
31
32#include <cassert>
33
34
35namespace theplu {
36namespace statistics {
37namespace regression {
38
39  ///
40  /// @todo document
41  ///
42  class PolynomialWeighted : public OneDimensionalWeighted
43  {
44  public:
45
46    ///
47    /// @param power degree of polynomial model
48    ///
49    inline PolynomialWeighted(size_t power)
50      : OneDimensionalWeighted(), power_(power) {}
51
52    ///
53    /// @brief Destructor
54    ///
55    inline ~PolynomialWeighted(void) {};
56
57    ///
58    /// This function computes the best-fit given the polynomial model
59    /// model by minimizing \f$ \sum{w_i(\hat{y_i}-y_i)^2} \f$, where
60    /// \f$ \hat{y} \f$ is the fitted value. The weight \f$ w_i \f$
61    /// should be proportional to the inverse of the variance for \f$
62    /// y_i \f$
63    ///
64    void fit(const utility::vector& x, const utility::vector& y,
65             const utility::vector& w);
66
67    ///
68    /// @return parameters for polynomial model
69    ///
70    utility::vector fit_parameters(void) { return md_.fit_parameters(); }
71
72    ///
73    /// function predicting in one point.
74    ///
75    double predict(const double x) const;
76
77    ///
78    /// @return expected prediction error for a new data point in @a x
79    /// with weight @a w
80    ///
81    double prediction_error(const double x, const double w=1) const;
82
83    ///
84    /// @return error of model value in @a x
85    ///
86    double standard_error(const double x) const;
87
88  private:
89    MultiDimensionalWeighted md_;
90    size_t power_;
91
92  };
93
94
95}}} // of namespaces regression, statisitcs and thep
96
97#endif
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