source: trunk/yat/regression/Naive.h @ 729

Last change on this file since 729 was 729, checked in by Peter, 15 years ago

Fixes #159. Also removed some inlines in OneDimensionalWeighted? by adding source file. Refs #81

  • 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 729 2007-01-05 16:00:15Z 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    /// This function computes the best-fit for the naive model \f$ y
61    /// = m \f$ from vectors \a x and \a y, by minimizing \f$
62    /// \sum{(y_i-m)^2} \f$.
63    ///
64    void fit(const utility::vector& x, const utility::vector& y);
65
66    ///
67    /// The predicted value is the average \f$ m \f$
68    ///
69    double predict(const double x) const;
70 
71    /**
72       \f$ \frac{\sum \epsilon_i^2}{N-1} \f$
73
74       @return Conditional variance
75    */
76    double s2(void) const;
77
78    ///
79    /// \f$ \frac{s^2}{N} \f$
80    ///
81    /// @return squared standard error
82    ///
83    /// @see statistics::Averager
84    ///
85    double standard_error2(const double x) const;
86
87  private:
88    ///
89    /// @brief The copy constructor (not implemented).
90    ///
91    Naive(const Naive&);
92
93    double mse_;
94  };
95
96}}} // of namespaces regression, yat, and theplu
97
98#endif
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