source: trunk/yat/statistics/PolynomialWeighted.h @ 681

Last change on this file since 681 was 681, checked in by Jari Häkkinen, 16 years ago

Moved namespace regression up one level (leaving namespace statistics).

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.4 KB
Line 
1#ifndef _theplu_yat_regression_polynomialweighted_
2#define _theplu_yat_regression_polynomialweighted_
3
4// $Id: PolynomialWeighted.h 681 2006-10-11 21:38:46Z 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 "OneDimensionalWeighted.h"
28#include "MultiDimensionalWeighted.h"
29#include "yat/utility/vector.h"
30
31#include <cassert>
32
33namespace theplu {
34namespace yat {
35namespace regression {
36
37  ///
38  /// @todo document
39  ///
40  class PolynomialWeighted : public OneDimensionalWeighted
41  {
42  public:
43
44    ///
45    /// @param power degree of polynomial model
46    ///
47    inline PolynomialWeighted(size_t power)
48      : OneDimensionalWeighted(), power_(power) {}
49
50    ///
51    /// @brief Destructor
52    ///
53    inline ~PolynomialWeighted(void) {};
54
55    ///
56    /// This function computes the best-fit given the polynomial model
57    /// model by minimizing \f$ \sum{w_i(\hat{y_i}-y_i)^2} \f$, where
58    /// \f$ \hat{y} \f$ is the fitted value. The weight \f$ w_i \f$
59    /// should be proportional to the inverse of the variance for \f$
60    /// y_i \f$
61    ///
62    void fit(const utility::vector& x, const utility::vector& y,
63             const utility::vector& w);
64
65    ///
66    /// @return parameters for polynomial model
67    ///
68    utility::vector fit_parameters(void) { return md_.fit_parameters(); }
69
70    ///
71    /// function predicting in one point.
72    ///
73    double predict(const double x) const;
74
75    ///
76    /// @return expected prediction error for a new data point in @a x
77    /// with weight @a w
78    ///
79    double prediction_error(const double x, const double w=1) const;
80
81    ///
82    /// @return error of model value in @a x
83    ///
84    double standard_error(const double x) const;
85
86  private:
87    MultiDimensionalWeighted md_;
88    size_t power_;
89
90  };
91
92}}} // of namespaces regression, yat, and theplu
93
94#endif
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