source: trunk/yat/regression/NaiveWeighted.h @ 1615

Last change on this file since 1615 was 1487, checked in by Jari Häkkinen, 13 years ago

Addresses #436. GPL license copy reference should also be updated.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.6 KB
Line 
1#ifndef _theplu_yat_regression_naiveweighted_
2#define _theplu_yat_regression_naiveweighted_
3
4// $Id: NaiveWeighted.h 1487 2008-09-10 08:41:36Z jari $
5
6/*
7  Copyright (C) 2005 Peter Johansson
8  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson
9  Copyright (C) 2008 Peter Johansson
10
11  This file is part of the yat library, http://dev.thep.lu.se/yat
12
13  The yat library is free software; you can redistribute it and/or
14  modify it under the terms of the GNU General Public License as
15  published by the Free Software Foundation; either version 3 of the
16  License, or (at your option) any later version.
17
18  The yat library is distributed in the hope that it will be useful,
19  but WITHOUT ANY WARRANTY; without even the implied warranty of
20  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
21  General Public License for more details.
22
23  You should have received a copy of the GNU General Public License
24  along with yat. If not, see <http://www.gnu.org/licenses/>.
25*/
26
27#include "OneDimensionalWeighted.h"
28
29#include <cmath>
30#include <iostream>
31#include <utility>
32
33namespace theplu {
34namespace yat {
35  namespace utility {
36    class VectorBase;
37  }
38namespace regression {
39
40  ///
41  /// @brief naive fitting.
42  ///
43  /// @todo document
44  ///
45  class NaiveWeighted : public OneDimensionalWeighted
46  {
47 
48  public:
49    ///
50    /// @brief The default constructor
51    ///
52    NaiveWeighted(void);
53
54    ///
55    /// @brief The destructor
56    ///
57    virtual ~NaiveWeighted(void);
58         
59    /**
60       This function computes the best-fit for the naive model \f$ y
61       = m \f$ from vectors \a x and \a y, by minimizing \f$ \sum
62       w_i(y_i-m)^2 \f$. The weight \f$ w_i \f$ is proportional to
63       the inverse of the variance for \f$ y_i \f$
64    */
65    void fit(const utility::VectorBase& x,
66             const utility::VectorBase& y,
67             const utility::VectorBase& w);
68
69    ///
70    /// Function predicting value using the naive model, i.e. a
71    /// weighted average.
72    ///
73    double predict(const double x) const;
74
75    /**
76       \f$ \frac{\sum w_i\epsilon_i^2}{ w \left(\frac{\left(\sum
77       w_i\right)^2}{\sum w_i^2}-1\right)} \f$
78
79       Rescaling all weights, both in fit and the passed @a w, results
80       in the same returned value.
81
82       @return Conditional variance of Y with weight @a w.
83    */
84    double s2(const double w=1) const;
85
86    /**
87       \f$ \frac{\sum w_i\epsilon_i^2}{ \left(\frac{\left(\sum
88       w_i\right)^2}{\sum w_i^2}-1\right)\sum w_i} \f$
89
90       @return estimated squared error of model value in @a x
91    */
92    double standard_error2(const double x) const;
93
94  private:
95    ///
96    /// Copy Constructor. (not implemented)
97    ///
98    NaiveWeighted(const NaiveWeighted&);
99
100  };
101
102}}} // of namespaces regression, yat, and theplu
103
104#endif
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