source: trunk/yat/regression/Naive.h @ 728

Last change on this file since 728 was 728, checked in by Peter, 15 years ago

added virtual function s2 in OneDimensional?.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.2 KB
Line 
1#ifndef _theplu_yat_regression_naive_
2#define _theplu_yat_regression_naive_
3
4// $Id: Naive.h 728 2007-01-04 16:07:16Z peter $
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 "OneDimensional.h"
28
29#include <iostream>
30#include <utility>
31
32namespace theplu {
33namespace yat {
34  namespace utility {
35    class vector;
36  }
37namespace regression {
38
39  /**
40     @brief Naive Regression
41   
42     Data are modeled as \f$ y_i = \alpha + \epsilon_i \f$
43
44  */ 
45  class Naive : public OneDimensional
46  {
47 
48  public:
49    ///
50    /// @brief The default constructor
51    ///
52    Naive(void);
53
54    ///
55    /// @brief The destructor
56    ///
57    virtual ~Naive(void);
58         
59    /**
60        Chi-squared \f$ \sum (x_i-m)^2 \f$
61    */
62    double chisq(void) const; 
63
64    ///
65    /// This function computes the best-fit for the naive model \f$ y
66    /// = m \f$ from vectors \a x and \a y, by minimizing \f$
67    /// \sum{(y_i-m)^2} \f$.
68    ///
69    void fit(const utility::vector& x, const utility::vector& y);
70
71    ///
72    /// The predicted value is the average \f$ m \f$
73    ///
74    double predict(const double x) const;
75 
76    /**
77       \f$ \frac{\sum \epsilon_i^2}{N-1} \f$
78
79       @return variance of residuals
80    */
81    double s2(void) const;
82
83    ///
84    /// @return standard error
85    ///
86    /// @see statistics::Averager
87    ///
88    double standard_error2(const double x) const;
89
90  private:
91    ///
92    /// @brief The copy constructor (not implemented).
93    ///
94    Naive(const Naive&);
95
96    double mse_;
97  };
98
99}}} // of namespaces regression, yat, and theplu
100
101#endif
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