source: trunk/yat/regression/MultiDimensional.h @ 1120

Last change on this file since 1120 was 1120, checked in by Peter, 14 years ago

vector is now Vector

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.5 KB
Line 
1#ifndef _theplu_yat_regression_multidimensional_
2#define _theplu_yat_regression_multidimensional_
3
4// $Id: MultiDimensional.h 1120 2008-02-21 23:18:41Z peter $
5
6/*
7  Copyright (C) 2005, 2006, 2007 Jari Häkkinen, Peter Johansson
8
9  This file is part of the yat library, http://trac.thep.lu.se/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/VectorBase.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       \brief Function fitting parameters of the linear model by
60       miminizing the quadratic deviation between model and data.
61
62       \throw A GSL_error exception is thrown if memory allocation
63       fails or the underlying GSL calls fails (usually matrix
64       dimension errors).
65    */
66    void fit(const utility::matrix& X, const utility::VectorBase& y);
67
68    ///
69    /// @return parameters of the model
70    ///
71    const utility::Vector& fit_parameters(void) const;
72
73    /**
74       @brief Summed Squared Error
75     */
76    double chisq(void) const;
77
78    ///
79    /// @return value in @a x according to fitted model
80    ///
81    double predict(const utility::VectorBase& x) const;
82
83    ///
84    /// @return expected squared prediction error for a new data point
85    /// in @a x
86    ///
87    double prediction_error2(const utility::VectorBase& x) const;
88
89    ///
90    /// @return squared error of model value in @a x
91    ///
92    double standard_error2(const utility::VectorBase& x) const;
93
94  private:
95    double chisquare_;
96    double s2_;
97    utility::matrix covariance_;
98    utility::Vector fit_parameters_;
99    gsl_multifit_linear_workspace* work_;
100
101  };
102
103}}} // of namespaces regression, yat, and theplu
104
105#endif
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