source: trunk/yat/regression/PolynomialWeighted.h @ 1000

Last change on this file since 1000 was 1000, checked in by Jari Häkkinen, 15 years ago

trac moved to new location.

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1#ifndef _theplu_yat_regression_polynomialweighted_
2#define _theplu_yat_regression_polynomialweighted_
3
4// $Id: PolynomialWeighted.h 1000 2007-12-23 20:09:15Z jari $
5
6/*
7  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson
8
9  This file is part of the yat library, http://trac.thep.lu.se/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
31namespace theplu {
32namespace yat {
33namespace regression {
34
35  ///
36  /// @brief Polynomial Regression in weighted fashion.
37  ///
38  class PolynomialWeighted : public OneDimensionalWeighted
39  {
40  public:
41
42    ///
43    /// @param power degree of polynomial model
44    ///
45    PolynomialWeighted(size_t power);
46
47    ///
48    /// @brief Destructor
49    ///
50    ~PolynomialWeighted(void);
51
52    ///
53    /// This function computes the best-fit given the polynomial model
54    /// model by minimizing \f$ \sum{w_i(\hat{y_i}-y_i)^2} \f$, where
55    /// \f$ \hat{y} \f$ is the fitted value. The weight \f$ w_i \f$
56    /// should be proportional to the inverse of the variance for \f$
57    /// y_i \f$
58    ///
59    void fit(const utility::vector& x, const utility::vector& y,
60             const utility::vector& w);
61
62    ///
63    /// @return parameters of the model
64    ///
65    /// @see MultiDimensional
66    ///
67    const utility::vector& fit_parameters(void) const;
68
69    ///
70    /// @return parameters for polynomial model
71    ///
72    utility::vector fit_parameters(void) { return md_.fit_parameters(); }
73
74    ///
75    /// @brief Mean Squared Error
76    ///
77    double s2(const double w=1) const;
78
79    ///
80    /// function predicting in one point.
81    ///
82    double predict(const double x) const;
83
84    ///
85    /// @return error of model value in @a x
86    ///
87    double standard_error2(const double x) const;
88
89  private:
90    MultiDimensionalWeighted md_;
91    size_t power_;
92
93  };
94
95}}} // of namespaces regression, yat, and theplu
96
97#endif
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