source: trunk/yat/regression/MultiDimensional.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 Id
File size: 2.3 KB
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1#ifndef _theplu_yat_regression_multidimensional_
2#define _theplu_yat_regression_multidimensional_
3
4// $Id: MultiDimensional.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
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/utility/matrix.h"
28#include "yat/utility/vector.h"
29
30#include <gsl/gsl_multifit.h>
31
32namespace theplu {
33namespace yat {
34namespace regression {
35
36  ///
37  /// @brief MultiDimesional fitting.
38  ///
39  class MultiDimensional
40  {
41  public:
42
43    ///
44    /// @brief Default Constructor
45    ///
46    MultiDimensional(void);
47
48    ///
49    /// @brief Destructor
50    ///
51    ~MultiDimensional(void);
52
53    ///
54    /// @brief covariance of parameters
55    ///
56    const utility::matrix& covariance(void) const;
57
58    ///
59    /// Function fitting parameters of the linear model by miminizing
60    /// the quadratic deviation between model and data.
61    ///
62    void fit(const utility::matrix& X, const utility::vector& y);
63
64    ///
65    /// @return parameters of the model
66    ///
67    const utility::vector& fit_parameters(void) const;
68
69    /**
70       @brief Mean Squared Error
71     */
72    double chisq(void) const;
73
74    ///
75    /// @return value in @a x according to fitted model
76    ///
77    double predict(const utility::vector& x) const;
78
79    ///
80    /// @return expected prediction error for a new data point in @a x
81    ///
82    double prediction_error(const utility::vector& x) const;
83
84    ///
85    /// @return error of model value in @a x
86    ///
87    double standard_error(const utility::vector& x) const;
88
89  private:
90    double chisquare_;
91    utility::matrix covariance_;
92    utility::vector fit_parameters_;
93    gsl_multifit_linear_workspace* work_;
94
95  };
96
97}}} // of namespaces regression, yat, and theplu
98
99#endif
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