source:trunk/lib/statistics/Naive.h@430

Last change on this file since 430 was 430, checked in by Peter, 17 years ago

changed interface of Regression::Local

• Property svn:eol-style set to native
• Property svn:keywords set to Author Date Id Revision
File size: 1.6 KB
Line
1// $Id: Naive.h 430 2005-12-08 22:53:08Z peter$
2
3#ifndef _theplu_statistics_regression_naive_
4#define _theplu_statistics_regression_naive_
5
6#include <c++_tools/statistics/OneDimensional.h>
7
8#include <c++_tools/gslapi/vector.h>
9
10#include <iostream>
11#include <utility>
12
13
14namespace theplu {
15namespace statistics {
16namespace regression {
17
18  ///
19  /// @bief naive fitting.
20  ///
21  /// @todo document
22  ///
23  class Naive : public OneDimensional
24  {
25
26  public:
27    ///
28    /// Default Constructor.
29    ///
30    inline Naive(void) : OneDimensional(), m_(0.0), m_err_(0.0) {}
31
32    ///
33    /// Copy Constructor. (not implemented)
34    ///
35    Naive(const Naive&);
36
37    ///
38    /// Destructor
39    ///
40    virtual ~Naive(void) {};
41
42    ///
43    /// This function computes the best-fit for the naive model \f$y 44 /// = m \f$ from vectors \a x and \a y, by minimizing \f$45 /// \sum{(y_i-m)^2} \f$. This function is the same as using the
46    /// weighted version with unity weights.
47    ///
48    void fit(const gslapi::vector& x, const gslapi::vector& y);
49
50    ///
51    /// Function predicting value using the naive model. \a y_err is
52    /// the expected deviation from the line for a new data point. The
53    /// error has two components: the variance of point and error in
54    /// estimation of the mean.
55    ///
56    void predict(const double x, double& y, double& y_err) const;
57
58    ///
59    /// @return header for print()
60    ///
62
63
64  private:
65    double s2_; // noise level - the typical variance for a point with
66                // weight w is s2/w
67    double m_;
68    double m_err_; // error of estimation of mean m_
69
70  };
71
72
73}}} // of namespaces regression, statisitcs and thep
74
75#endif
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