1 | // $Id: RegressionLocal.h 235 2005-02-21 14:53:48Z peter $ |
---|
2 | |
---|
3 | #ifndef _theplu_statistics_regression_local_ |
---|
4 | #define _theplu_statistics_regression_local_ |
---|
5 | |
---|
6 | // C++ tools include |
---|
7 | ///////////////////// |
---|
8 | #include "Regression.h" |
---|
9 | #include "RegressionKernel.h" |
---|
10 | #include "vector.h" |
---|
11 | |
---|
12 | // Standard C++ includes |
---|
13 | //////////////////////// |
---|
14 | |
---|
15 | |
---|
16 | namespace theplu { |
---|
17 | namespace statistics { |
---|
18 | |
---|
19 | |
---|
20 | /// |
---|
21 | /// Class for Locally-weighted regression. |
---|
22 | /// |
---|
23 | |
---|
24 | class RegressionLocal |
---|
25 | { |
---|
26 | |
---|
27 | public: |
---|
28 | /// |
---|
29 | /// Default Constructor. |
---|
30 | /// |
---|
31 | RegressionLocal(void); |
---|
32 | |
---|
33 | /// |
---|
34 | /// Constructor taking type of \a regressor, |
---|
35 | /// type of \a kernel. |
---|
36 | /// |
---|
37 | RegressionLocal(Regression& regressor, RegressionKernel& kernel); |
---|
38 | |
---|
39 | /// |
---|
40 | /// Copy Constructor. (Not implemented) |
---|
41 | /// |
---|
42 | RegressionLocal(const RegressionLocal&); |
---|
43 | |
---|
44 | /// |
---|
45 | /// Destructor |
---|
46 | /// |
---|
47 | virtual ~RegressionLocal(void) {}; |
---|
48 | |
---|
49 | |
---|
50 | /// |
---|
51 | /// adding a data point |
---|
52 | /// |
---|
53 | inline void add(const double x, const double y) |
---|
54 | { data_.push_back(std::make_pair(x,y)); } |
---|
55 | |
---|
56 | /// |
---|
57 | /// Function returning the points where to predict |
---|
58 | /// |
---|
59 | inline const std::vector<double>& x(void) const { return x_; } |
---|
60 | |
---|
61 | /// |
---|
62 | /// Function returning predicted values |
---|
63 | /// |
---|
64 | inline const std::vector<double>& y(void) const { return y_; } |
---|
65 | |
---|
66 | /// |
---|
67 | /// Function returning error of predictions |
---|
68 | /// |
---|
69 | inline const std::vector<double>& y_err(void) const { return y_err_; } |
---|
70 | |
---|
71 | /// |
---|
72 | /// Performs the fit in data defined by add using a kernel and a |
---|
73 | /// regression method defined in the constructor. The algorithm |
---|
74 | /// selects boundaries for the kernel such that \a fraction of the |
---|
75 | /// data points are used and the point where the fit is done is in |
---|
76 | /// the middle. Starting with the smallest x, the function jumps |
---|
77 | /// \a step_size point in each iteration to do the next fit |
---|
78 | /// |
---|
79 | void fit(const double fraction, const u_int step_size=1); |
---|
80 | |
---|
81 | /// |
---|
82 | /// @return prediction values and parameters |
---|
83 | /// |
---|
84 | std::ostream& print(std::ostream&) const; |
---|
85 | |
---|
86 | /// |
---|
87 | /// @return header for print() |
---|
88 | /// |
---|
89 | inline std::ostream& print_header(std::ostream& s) const |
---|
90 | { return regressor_->print_header(s); } |
---|
91 | |
---|
92 | |
---|
93 | private: |
---|
94 | std::vector<std::pair<double, double> > data_; |
---|
95 | gslapi::vector data_y_; |
---|
96 | RegressionKernel* kernel_; |
---|
97 | Regression* regressor_; |
---|
98 | std::vector<double> x_; |
---|
99 | std::vector<double> y_; |
---|
100 | std::vector<double> y_err_; |
---|
101 | |
---|
102 | |
---|
103 | }; |
---|
104 | |
---|
105 | }} // of namespace statistics and namespace theplu |
---|
106 | |
---|
107 | #endif |
---|
108 | |
---|