source: trunk/c++_tools/statistics/Linear.h @ 675

Last change on this file since 675 was 675, checked in by Jari Häkkinen, 15 years ago

References #83. Changing project name to yat. Compilation will fail in this revision.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 3.1 KB
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1#ifndef _theplu_statistics_regression_linear_
2#define _theplu_statistics_regression_linear_
3
4// $Id: Linear.h 675 2006-10-10 12:08:45Z 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 "yat/statistics/OneDimensional.h"
28
29#include <cmath>
30
31namespace theplu {
32  namespace utility {
33    class vector;
34  }
35namespace statistics { 
36namespace regression {
37
38  ///
39  /// @brief linear regression.   
40  ///
41  /// @todo document
42  ///
43  class Linear : public OneDimensional
44  {
45 
46  public:
47    ///
48    /// Default Constructor.
49    ///
50    inline Linear(void)
51      : OneDimensional(), alpha_(0), alpha_var_(0), beta_(0), beta_var_(0),
52        m_x_(0){}
53
54    ///
55    /// Destructor
56    ///
57    inline virtual ~Linear(void) {};
58         
59    ///
60    /// @return the parameter \f$ \alpha \f$
61    ///
62    inline double alpha(void) const { return alpha_; }
63
64    ///
65    /// @return standard deviation of parameter \f$ \alpha \f$
66    ///
67    inline double alpha_err(void) const { return sqrt(alpha_var_); }
68
69    ///
70    /// @return the parameter \f$ \beta \f$
71    ///
72    inline double beta(void) const { return beta_; }
73
74    ///
75    /// @return standard deviation of parameter \f$ \beta \f$
76    ///
77    inline double beta_err(void) const { return sqrt(beta_var_); }
78   
79    ///
80    /// This function computes the best-fit linear regression
81    /// coefficients \f$ (\alpha, \beta)\f$ of the model \f$ y =
82    /// \alpha + \beta (x-m_x) \f$ from vectors \a x and \a y, by
83    /// minimizing \f$ \sum{(y_i - \alpha - \beta (x-m_x))^2} \f$. By
84    /// construction \f$ \alpha \f$ and \f$ \beta \f$ are independent.
85    ///
86    void fit(const utility::vector& x, const utility::vector& y) ;
87   
88    ///
89    /// @return value in @a x of model
90    ///
91    double predict(const double x) const;
92
93    ///
94    /// @return expected prediction error for a new data point in @a x
95    ///
96    double prediction_error(const double x) const;
97
98    ///
99    /// @return error of model value in @a x
100    ///
101    double standard_error(const double x) const;
102
103    ///
104    /// Function returning the coefficient of determination,
105    /// i.e. fraction of variance explained by the linear model.
106    ///
107    inline double r2(void) const { return r2_; }
108
109  private:
110    ///
111    /// Copy Constructor. (not implemented)
112    ///
113    Linear(const Linear&);
114
115    double alpha_;
116    double alpha_var_;
117    double beta_;
118    double beta_var_;
119    double m_x_; // average of x values
120    double r2_; // coefficient of determination
121  };
122
123}}} // of namespaces regression, statisitcs and thep
124
125#endif
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