source: trunk/yat/regression/Naive.h @ 831

Last change on this file since 831 was 831, checked in by Peter, 15 years ago

Refs #185.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.2 KB
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1#ifndef _theplu_yat_regression_naive_
2#define _theplu_yat_regression_naive_
3
4// $Id: Naive.h 831 2007-03-27 13:22:09Z peter $
5
6/*
7  Copyright (C) 2004 Peter Johansson
8  Copyright (C) 2005, 2006 Jari Häkkinen, Peter Johansson
9  Copyright (C) 2007 Peter Johansson
10
11  This file is part of the yat library, http://lev.thep.lu.se/trac/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 "OneDimensional.h"
30
31#include <iostream>
32#include <utility>
33
34namespace theplu {
35namespace yat {
36  namespace utility {
37    class vector;
38  }
39namespace regression {
40
41  /**
42     @brief Naive Regression
43   
44     Data are modeled as \f$ y_i = \alpha + \epsilon_i \f$
45
46  */ 
47  class Naive : public OneDimensional
48  {
49 
50  public:
51    ///
52    /// @brief The default constructor
53    ///
54    Naive(void);
55
56    ///
57    /// @brief The destructor
58    ///
59    virtual ~Naive(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$
64    /// \sum{(y_i-m)^2} \f$.
65    ///
66    void fit(const utility::vector& x, const utility::vector& y);
67
68    ///
69    /// The predicted value is the average \f$ m \f$
70    ///
71    double predict(const double x) const;
72 
73    /**
74       \f$ \frac{\sum \epsilon_i^2}{N-1} \f$
75
76       @return Conditional variance
77    */
78    double s2(void) const;
79
80    ///
81    /// \f$ \frac{s^2}{N} \f$
82    ///
83    /// @return squared standard error
84    ///
85    /// @see statistics::Averager
86    ///
87    double standard_error2(const double x) const;
88
89  private:
90    ///
91    /// @brief The copy constructor (not implemented).
92    ///
93    Naive(const Naive&);
94
95    double mse_;
96  };
97
98}}} // of namespaces regression, yat, and theplu
99
100#endif
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