source: trunk/c++_tools/statistics/OneDimensionalWeighted.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: 2.4 KB
Line 
1#ifndef _theplu_statistics_regression_onedimensioanlweighted_
2#define _theplu_statistics_regression_onedimensioanlweighted_
3
4// $Id: OneDimensionalWeighted.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 <ostream>
28
29namespace theplu {
30namespace utility {
31  class vector;
32}
33
34namespace statistics {
35namespace regression {
36 
37  ///
38  /// Abstract Base Class for One Dimensional fitting in a weighted
39  /// fashion.
40  ///
41  /// @todo document
42  ///
43  class OneDimensionalWeighted
44  {
45 
46  public:
47    ///
48    /// Default Constructor.
49    ///
50    inline OneDimensionalWeighted(void):s2_(0)  {}
51
52    ///
53    /// Destructor
54    ///
55    virtual ~OneDimensionalWeighted(void) {};
56         
57    ///
58    /// This function computes the best-fit given a model (see
59    /// specific class for details) by minimizing \f$
60    /// \sum{w_i(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the
61    /// fitted value. The weight \f$ w_i \f$ should be proportional
62    /// to the inverse of the variance for \f$ y_i \f$
63    ///
64    virtual void fit(const utility::vector& x, const utility::vector& y, 
65                     const utility::vector& w)=0;
66
67    ///
68    /// function predicting in one point.
69    ///
70    virtual double predict(const double x) const=0;
71
72    ///
73    /// @return expected prediction error for a new data point in @a x
74    /// with weight @a w
75    ///
76    virtual double prediction_error(const double x, const double w=1) const=0;
77
78    ///
79    /// @return error of model value in @a x
80    ///
81    virtual double standard_error(const double x) const=0;
82
83  protected:
84    ///
85    /// noise level - the typical variance for a point with weight w
86    /// is s2/w
87    ///
88    double s2_; 
89  };
90
91}}} // of namespaces regression, statisitcs and thep
92
93#endif
Note: See TracBrowser for help on using the repository browser.