source: trunk/yat/regression/Local.h @ 726

Last change on this file since 726 was 726, checked in by Peter, 15 years ago

fixes #165 added test checking Linear Regression is equivalent to Polynomial regression of degree one.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.8 KB
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1#ifndef _theplu_yat_regression_local_
2#define _theplu_yat_regression_local_
3
4// $Id: Local.h 726 2007-01-04 14:38:56Z peter $
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 "Kernel.h"
28#include "OneDimensionalWeighted.h"
29#include "yat/utility/vector.h"
30
31#include <iostream>
32
33namespace theplu {
34namespace yat {
35namespace regression {
36
37  ///
38  /// Class for Locally weighted regression.
39  ///
40  /// Locally weighted regression is an algorithm for learning
41  /// continuous non-linear mappings in a non-parametric manner.  In
42  /// locally weighted regression, points are weighted by proximity to
43  /// the current x in question using a Kernel. A weighted regression
44  /// is then computed using the weighted points and a specific
45  /// Regression method. This procedure is repeated, which results in
46  /// a pointwise approximation of the underlying (unknown) function.
47  ///
48  class Local
49  {
50 
51  public:
52    ///
53    /// @brief Constructor taking type of \a regressor,
54    /// type of \a kernel.
55    ///
56    Local(OneDimensionalWeighted& r, Kernel& k);
57
58    ///
59    /// @brief The destructor
60    ///
61    virtual ~Local(void);
62
63    ///
64    /// adding a data point
65    ///
66    void add(const double x, const double y);
67
68    ///
69    /// @param nof_points Number of points used in each fit
70    /// @param step_size Size of step between each fit
71    ///
72    void fit(const size_t step_size, const size_t nof_points);
73
74    ///
75    /// @return x-values where fitting was performed.
76    ///
77    const utility::vector& x(void) const;
78
79    ///
80    /// Function returning predicted values
81    ///
82    const utility::vector& y_predicted(void) const;
83
84    ///
85    /// Function returning error of predictions
86    ///
87    const utility::vector& y_err(void) const;
88
89  private:
90    ///
91    /// Copy Constructor. (Not implemented)
92    ///
93    Local(const Local&);
94
95    std::vector<std::pair<double, double> > data_;
96    Kernel* kernel_;
97    OneDimensionalWeighted* regressor_;
98    utility::vector x_;
99    utility::vector y_predicted_; 
100    utility::vector y_err_; 
101  };
102
103  ///
104  /// The output operator for the Regression::Local class.
105  ///
106  std::ostream& operator<<(std::ostream&, const Local& );
107
108}}} // of namespaces regression, yat, and theplu
109
110#endif
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