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