source: trunk/yat/regression/NaiveWeighted.h @ 1401

Last change on this file since 1401 was 1275, checked in by Jari Häkkinen, 14 years ago

Updating copyright statements.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.7 KB
Line 
1#ifndef _theplu_yat_regression_naiveweighted_
2#define _theplu_yat_regression_naiveweighted_
3
4// $Id: NaiveWeighted.h 1275 2008-04-11 06:10:12Z jari $
5
6/*
7  Copyright (C) 2005 Peter Johansson
8  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson
9  Copyright (C) 2008 Peter Johansson
10
11  This file is part of the yat library, http://trac.thep.lu.se/yat
12
13  The yat library is free software; you can redistribute it and/or
14  modify it under the terms of the GNU General Public License as
15  published by the Free Software Foundation; either version 2 of the
16  License, or (at your option) any later version.
17
18  The yat library is distributed in the hope that it will be useful,
19  but WITHOUT ANY WARRANTY; without even the implied warranty of
20  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
21  General Public License for more details.
22
23  You should have received a copy of the GNU General Public License
24  along with this program; if not, write to the Free Software
25  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
26  02111-1307, USA.
27*/
28
29#include "OneDimensionalWeighted.h"
30
31#include <cmath>
32#include <iostream>
33#include <utility>
34
35namespace theplu {
36namespace yat {
37  namespace utility {
38    class VectorBase;
39  }
40namespace regression {
41
42  ///
43  /// @brief naive fitting.
44  ///
45  /// @todo document
46  ///
47  class NaiveWeighted : public OneDimensionalWeighted
48  {
49 
50  public:
51    ///
52    /// @brief The default constructor
53    ///
54    NaiveWeighted(void);
55
56    ///
57    /// @brief The destructor
58    ///
59    virtual ~NaiveWeighted(void);
60         
61    /**
62       This function computes the best-fit for the naive model \f$ y
63       = m \f$ from vectors \a x and \a y, by minimizing \f$ \sum
64       w_i(y_i-m)^2 \f$. The weight \f$ w_i \f$ is proportional to
65       the inverse of the variance for \f$ y_i \f$
66    */
67    void fit(const utility::VectorBase& x,
68             const utility::VectorBase& y,
69             const utility::VectorBase& w);
70
71    ///
72    /// Function predicting value using the naive model, i.e. a
73    /// weighted average.
74    ///
75    double predict(const double x) const;
76
77    /**
78       \f$ \frac{\sum w_i\epsilon_i^2}{ w \left(\frac{\left(\sum
79       w_i\right)^2}{\sum w_i^2}-1\right)} \f$
80
81       Rescaling all weights, both in fit and the passed @a w, results
82       in the same returned value.
83
84       @return Conditional variance of Y with weight @a w.
85    */
86    double s2(const double w=1) const;
87
88    /**
89       \f$ \frac{\sum w_i\epsilon_i^2}{ \left(\frac{\left(\sum
90       w_i\right)^2}{\sum w_i^2}-1\right)\sum w_i} \f$
91
92       @return estimated squared error of model value in @a x
93    */
94    double standard_error2(const double x) const;
95
96  private:
97    ///
98    /// Copy Constructor. (not implemented)
99    ///
100    NaiveWeighted(const NaiveWeighted&);
101
102  };
103
104}}} // of namespaces regression, yat, and theplu
105
106#endif
Note: See TracBrowser for help on using the repository browser.