# source:trunk/yat/regression/Naive.h@726

Last change on this file since 726 was 726, checked in by Peter, 16 years ago

fixes #165 added test checking Linear Regression is equivalent to Polynomial regression of degree one.

• 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// $Id: Naive.h 726 2007-01-04 14:38:56Z peter$
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
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    /// @return standard error
78    ///
79    /// @see statistics::Averager
80    ///
81    double standard_error(const double x) const;
82
83  private:
84    ///
85    /// @brief The copy constructor (not implemented).
86    ///
87    Naive(const Naive&);
88
89    double mse_;
90  };
91
92}}} // of namespaces regression, yat, and theplu
93
94#endif
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