source: trunk/yat/regression/Linear.h @ 703

Last change on this file since 703 was 703, checked in by Jari Häkkinen, 16 years ago

Addresses #65 and #170.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 3.0 KB
Line 
1#ifndef _theplu_yat_regression_linear_
2#define _theplu_yat_regression_linear_
3
4// $Id: Linear.h 703 2006-12-18 00:47:44Z jari $
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 <cmath>
30
31namespace theplu {
32namespace yat {
33  namespace utility {
34    class vector;
35  }
36namespace regression {
37
38  ///
39  /// @brief linear regression.   
40  ///
41  /// @todo document
42  ///
43  class Linear : public OneDimensional
44  {
45 
46  public:
47    ///
48    /// @brief The default constructor
49    ///
50    Linear(void);
51
52    ///
53    /// @brief The destructor
54    ///
55    virtual ~Linear(void);
56         
57    ///
58    /// @return the parameter \f$ \alpha \f$
59    ///
60    inline double alpha(void) const { return alpha_; }
61
62    ///
63    /// @return standard deviation of parameter \f$ \alpha \f$
64    ///
65    inline double alpha_err(void) const { return sqrt(alpha_var_); }
66
67    ///
68    /// @return the parameter \f$ \beta \f$
69    ///
70    inline double beta(void) const { return beta_; }
71
72    ///
73    /// @return standard deviation of parameter \f$ \beta \f$
74    ///
75    inline double beta_err(void) const { return sqrt(beta_var_); }
76   
77    ///
78    /// This function computes the best-fit linear regression
79    /// coefficients \f$ (\alpha, \beta)\f$ of the model \f$ y =
80    /// \alpha + \beta (x-m_x) \f$ from vectors \a x and \a y, by
81    /// minimizing \f$ \sum{(y_i - \alpha - \beta (x-m_x))^2} \f$. By
82    /// construction \f$ \alpha \f$ and \f$ \beta \f$ are independent.
83    ///
84    void fit(const utility::vector& x, const utility::vector& y) ;
85   
86    ///
87    /// @brief Mean Squared Error
88    ///
89    inline double mse(void) const { return mse_; }
90
91    ///
92    /// @return value in @a x of model
93    ///
94    double predict(const double x) const;
95
96    ///
97    /// @return error of model value in @a x
98    ///
99    double standard_error(const double x) const;
100
101    ///
102    /// Function returning the coefficient of determination,
103    /// i.e. fraction of variance explained by the linear model.
104    ///
105    inline double r2(void) const { return r2_; }
106
107  private:
108    ///
109    /// Copy Constructor. (not implemented)
110    ///
111    Linear(const Linear&);
112
113    double alpha_;
114    double alpha_var_;
115    double beta_;
116    double beta_var_;
117    double mse_;
118    double m_x_; // average of x values
119    double r2_; // coefficient of determination
120  };
121
122}}} // of namespaces regression, yat, and theplu
123
124#endif
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