1 | #ifndef _theplu_statistics_regression_naive_ |
---|
2 | #define _theplu_statistics_regression_naive_ |
---|
3 | |
---|
4 | // $Id: Naive.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 <iostream> |
---|
30 | #include <utility> |
---|
31 | |
---|
32 | namespace theplu { |
---|
33 | namespace utility { |
---|
34 | class vector; |
---|
35 | } |
---|
36 | namespace statistics { |
---|
37 | namespace regression { |
---|
38 | |
---|
39 | /// |
---|
40 | /// @bief naive fitting. |
---|
41 | /// |
---|
42 | /// @todo document |
---|
43 | /// |
---|
44 | class Naive : public OneDimensional |
---|
45 | { |
---|
46 | |
---|
47 | public: |
---|
48 | /// |
---|
49 | /// Default Constructor. |
---|
50 | /// |
---|
51 | inline Naive(void) : OneDimensional(), m_(0.0), m_err_(0.0) {} |
---|
52 | |
---|
53 | /// |
---|
54 | /// Copy Constructor. (not implemented) |
---|
55 | /// |
---|
56 | Naive(const Naive&); |
---|
57 | |
---|
58 | /// |
---|
59 | /// Destructor |
---|
60 | /// |
---|
61 | virtual ~Naive(void) {}; |
---|
62 | |
---|
63 | /// |
---|
64 | /// This function computes the best-fit for the naive model \f$ y |
---|
65 | /// = m \f$ from vectors \a x and \a y, by minimizing \f$ |
---|
66 | /// \sum{(y_i-m)^2} \f$. This function is the same as using the |
---|
67 | /// weighted version with unity weights. |
---|
68 | /// |
---|
69 | void fit(const utility::vector& x, const utility::vector& y); |
---|
70 | |
---|
71 | /// |
---|
72 | /// Function predicting value using the naive model. |
---|
73 | /// |
---|
74 | double predict(const double x) const; |
---|
75 | |
---|
76 | /// |
---|
77 | /// The expected deviation from the line for a new data point. The |
---|
78 | /// error has two components: the variance of point and error in |
---|
79 | /// estimation of the mean. |
---|
80 | /// |
---|
81 | double prediction_error(const double x) const; |
---|
82 | |
---|
83 | /// |
---|
84 | /// @return standard error |
---|
85 | /// |
---|
86 | double standard_error(const double x) const; |
---|
87 | |
---|
88 | private: |
---|
89 | double m_; |
---|
90 | double m_err_; // error of estimation of mean m_ |
---|
91 | |
---|
92 | }; |
---|
93 | |
---|
94 | |
---|
95 | }}} // of namespaces regression, statisitcs and thep |
---|
96 | |
---|
97 | #endif |
---|