source: trunk/c++_tools/statistics/NaiveWeighted.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: 2.6 KB
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1#ifndef _theplu_statistics_regression_naive_weighted_
2#define _theplu_statistics_regression_naive_weighted_
3
4// $Id: NaiveWeighted.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 "yat/statistics/OneDimensionalWeighted.h"
28
29#include <cmath>
30#include <iostream>
31#include <utility>
32
33
34namespace theplu {
35  namespace utility {
36    class vector;
37  }
38namespace statistics {
39namespace regression {
40
41  ///
42  /// @brief naive fitting.
43  ///
44  /// @todo document
45  ///
46  class NaiveWeighted : public OneDimensionalWeighted
47  {
48 
49  public:
50    ///
51    /// Default Constructor.
52    ///
53    inline NaiveWeighted(void) 
54      : OneDimensionalWeighted(), m_(0.0), m_err_(0.0) {}
55
56    ///
57    /// 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::vector& x,
68             const utility::vector& y,
69             const utility::vector& w);
70
71    ///
72    /// Function predicting value using the naive model, i.e. a
73    /// weighted average.
74    ///
75    inline double predict(const double x) const { return m_; }
76
77    ///
78    /// @return expected prediction error for a new data point in @a x
79    /// with weight @a w
80    ///
81    inline double prediction_error(const double x, const double w=1) const
82    { return sqrt(m_err_*m_err_ + s2_/w); }
83
84    ///
85    /// @return estimation of error of model value in @a x
86    ///
87    inline double standard_error(const double x) const
88    { return m_err_; }
89
90  private:
91    ///
92    /// Copy Constructor. (not implemented)
93    ///
94    NaiveWeighted(const NaiveWeighted&);
95
96    double m_;
97    double m_err_; // error of estimation of mean m_
98
99  };
100
101
102}}} // of namespaces regression, statisitcs and thep
103
104#endif
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