source: trunk/yat/regression/Naive.h

Last change on this file was 2119, checked in by Peter, 12 years ago

converted files to utf-8. fixes #577

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.1 KB
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1#ifndef _theplu_yat_regression_naive_
2#define _theplu_yat_regression_naive_
3
4// $Id: Naive.h 2119 2009-12-12 23:11:43Z peter $
5
6/*
7  Copyright (C) 2004 Peter Johansson
8  Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, Peter Johansson
9
10  This file is part of the yat library, http://dev.thep.lu.se/yat
11
12  The yat library is free software; you can redistribute it and/or
13  modify it under the terms of the GNU General Public License as
14  published by the Free Software Foundation; either version 3 of the
15  License, or (at your option) any later version.
16
17  The yat library is distributed in the hope that it will be useful,
18  but WITHOUT ANY WARRANTY; without even the implied warranty of
19  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
20  General Public License for more details.
21
22  You should have received a copy of the GNU General Public License
23  along with yat. If not, see <http://www.gnu.org/licenses/>.
24*/
25
26#include "OneDimensional.h"
27
28#include <utility>
29
30namespace theplu {
31namespace yat {
32  namespace utility {
33    class VectorBase;
34  }
35namespace regression {
36
37  /**
38     @brief Naive Regression
39   
40     Data are modeled as \f$ y_i = \alpha + \epsilon_i \f$
41
42  */ 
43  class Naive : public OneDimensional
44  {
45 
46  public:
47    ///
48    /// @brief The default constructor
49    ///
50    Naive(void);
51
52    ///
53    /// @brief The destructor
54    ///
55    virtual ~Naive(void);
56         
57    ///
58    /// This function computes the best-fit for the naive model \f$ y
59    /// = m \f$ from vectors \a x and \a y, by minimizing \f$
60    /// \sum{(y_i-m)^2} \f$.
61    ///
62    void fit(const utility::VectorBase& x, const utility::VectorBase& y);
63
64    ///
65    /// The predicted value is the average \f$ m \f$
66    ///
67    double predict(const double x) const;
68 
69    /**
70       \f$ \frac{\sum \epsilon_i^2}{N-1} \f$
71
72       @return Conditional variance
73    */
74    double s2(void) const;
75
76    ///
77    /// \f$ \frac{s^2}{N} \f$
78    ///
79    /// @return squared standard error
80    ///
81    /// @see statistics::Averager
82    ///
83    double standard_error2(const double x) const;
84
85  private:
86    ///
87    /// @brief The copy constructor (not implemented).
88    ///
89    Naive(const Naive&);
90
91    double mse_;
92  };
93
94}}} // of namespaces regression, yat, and theplu
95
96#endif
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