1 | #ifndef _theplu_statistics_regression_multidimensional_ |
---|
2 | #define _theplu_statistics_regression_multidimensional_ |
---|
3 | |
---|
4 | // $Id: MultiDimensional.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/utility/matrix.h" |
---|
28 | #include "yat/utility/vector.h" |
---|
29 | |
---|
30 | #include <gsl/gsl_multifit.h> |
---|
31 | |
---|
32 | |
---|
33 | namespace theplu { |
---|
34 | namespace statistics { |
---|
35 | namespace regression { |
---|
36 | |
---|
37 | /// |
---|
38 | /// @brief MultiDimesional fitting. |
---|
39 | /// |
---|
40 | class MultiDimensional |
---|
41 | { |
---|
42 | public: |
---|
43 | |
---|
44 | /// |
---|
45 | /// @brief Default Constructor |
---|
46 | /// |
---|
47 | inline MultiDimensional(void) : chisquare_(0), work_(NULL) {} |
---|
48 | |
---|
49 | /// |
---|
50 | /// @brief Destructor |
---|
51 | /// |
---|
52 | inline ~MultiDimensional(void) { if (work_) gsl_multifit_linear_free(work_);} |
---|
53 | |
---|
54 | /// |
---|
55 | /// Function fitting parameters of the linear model by miminizing |
---|
56 | /// the quadratic deviation between model and data. |
---|
57 | /// |
---|
58 | void fit(const utility::matrix& X, const utility::vector& y); |
---|
59 | |
---|
60 | /// |
---|
61 | /// @return parameters of the model |
---|
62 | /// |
---|
63 | utility::vector fit_parameters(void) { return fit_parameters_; } |
---|
64 | |
---|
65 | /// |
---|
66 | /// @return value in @a x according to fitted model |
---|
67 | /// |
---|
68 | inline double predict(const utility::vector& x) const |
---|
69 | { return fit_parameters_ * x; } |
---|
70 | |
---|
71 | /// |
---|
72 | /// @return expected prediction error for a new data point in @a x |
---|
73 | /// |
---|
74 | double prediction_error(const utility::vector& x) const; |
---|
75 | |
---|
76 | /// |
---|
77 | /// @return error of model value in @a x |
---|
78 | /// |
---|
79 | double standard_error(const utility::vector& x) const; |
---|
80 | |
---|
81 | private: |
---|
82 | double chisquare_; |
---|
83 | utility::matrix covariance_; |
---|
84 | utility::vector fit_parameters_; |
---|
85 | gsl_multifit_linear_workspace* work_; |
---|
86 | |
---|
87 | }; |
---|
88 | |
---|
89 | |
---|
90 | }}} // of namespaces regression, statisitcs and thep |
---|
91 | |
---|
92 | #endif |
---|