source: trunk/yat/statistics/NaiveWeighted.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: 2.5 KB
Line 
1#ifndef _theplu_yat_regression_naiveweighted_
2#define _theplu_yat_regression_naiveweighted_
3
4// $Id: NaiveWeighted.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 "OneDimensionalWeighted.h"
28
29#include <cmath>
30#include <iostream>
31#include <utility>
32
33namespace theplu {
34namespace yat {
35  namespace utility {
36    class vector;
37  }
38namespace regression {
39
40  ///
41  /// @brief naive fitting.
42  ///
43  /// @todo document
44  ///
45  class NaiveWeighted : public OneDimensionalWeighted
46  {
47 
48  public:
49    ///
50    /// Default Constructor.
51    ///
52    inline NaiveWeighted(void) 
53      : OneDimensionalWeighted(), m_(0.0), m_err_(0.0) {}
54
55    ///
56    /// Destructor
57    ///
58    virtual ~NaiveWeighted(void) {};
59         
60    ///
61    /// This function computes the best-fit for the naive model \f$ y
62    /// = m \f$ from vectors \a x and \a y, by minimizing \f$ \sum
63    /// w_i(y_i-m)^2 \f$. The weight \f$ w_i \f$ is proportional to
64    /// the inverse of the variance for \f$ y_i \f$
65    ///
66    void fit(const utility::vector& x,
67             const utility::vector& y,
68             const utility::vector& w);
69
70    ///
71    /// Function predicting value using the naive model, i.e. a
72    /// weighted average.
73    ///
74    inline double predict(const double x) const { return m_; }
75
76    ///
77    /// @return expected prediction error for a new data point in @a x
78    /// with weight @a w
79    ///
80    inline double prediction_error(const double x, const double w=1) const
81    { return sqrt(m_err_*m_err_ + s2_/w); }
82
83    ///
84    /// @return estimation of error of model value in @a x
85    ///
86    inline double standard_error(const double x) const
87    { return m_err_; }
88
89  private:
90    ///
91    /// Copy Constructor. (not implemented)
92    ///
93    NaiveWeighted(const NaiveWeighted&);
94
95    double m_;
96    double m_err_; // error of estimation of mean m_
97
98  };
99
100}}} // of namespaces regression, yat, and theplu
101
102#endif
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