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

Last change on this file since 729 was 729, checked in by Peter, 16 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 Id
File size: 3.3 KB
Line 
1#ifndef _theplu_yat_regression_linearweighted_
2#define _theplu_yat_regression_linearweighted_
3
4// $Id: LinearWeighted.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 "OneDimensionalWeighted.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 LinearWeighted : public OneDimensionalWeighted
44  {
45 
46  public:
47    ///
48    /// @brief The default constructor.
49    ///
50    LinearWeighted(void);
51
52    ///
53    /// @brief The destructor
54    ///
55    virtual ~LinearWeighted(void);
56         
57    ///
58    /// @return the parameter \f$ \alpha \f$
59    ///
60    double alpha(void) const;
61
62    ///
63    /// @return standard deviation of parameter \f$ \alpha \f$
64    ///
65    double alpha_var(void) const;
66
67    ///
68    /// @return the parameter \f$ \beta \f$
69    ///
70    double beta(void) const;
71
72    ///
73    /// @return standard deviation of parameter \f$ \beta \f$
74    ///
75    double beta_var(void) const;
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{w_i(y_i - \alpha - \beta (x-m_x))^2} \f$,
82       where \f$ m_x \f$ is the weighted average. By construction \f$
83       \alpha \f$ and \f$ \beta \f$ are independent.
84    **/
85    /// @todo calculate mse
86    void fit(const utility::vector& x, const utility::vector& y,
87             const utility::vector& w);
88   
89    ///
90    ///  Function predicting value using the linear model:
91    /// \f$ y =\alpha + \beta (x - m) \f$
92    ///
93    double predict(const double x) const { return alpha_ + beta_ * (x-m_x_); }
94
95    ///
96    /// estimated squared deviation from predicted value for a new
97    /// data point in @a x with weight @a w
98    ///
99    double prediction_error2(const double x, const double w=1) const;
100
101    /**
102       Noise level for points with weight @a w.
103    */
104    double s2(double w=1) const;
105
106    /**
107       estimated error \f$ y_{err} = \sqrt{ Var(\alpha) +
108       Var(\beta)*(x-m)} \f$.
109    */
110    double standard_error2(const double x) const;
111
112  private:
113    ///
114    /// Copy Constructor. (not implemented)
115    ///
116    LinearWeighted(const LinearWeighted&);
117
118    double m_x(void) const;
119    double m_y(void) const;
120    double sxx(void) const;
121    double syy(void) const;
122    double sxy(void) const;
123   
124    double alpha_;
125    double alpha_var_;
126    double beta_;
127    double beta_var_;
128    double m_x_; // average of x values
129    double r2_; // coefficient of determination
130    double s2_;
131    double mse_;
132  };
133
134}}} // of namespaces regression, yat, and theplu
135
136#endif
Note: See TracBrowser for help on using the repository browser.