# source:trunk/yat/regression/Local.h@2564

Last change on this file since 2564 was 2564, checked in by Peter, 10 years ago

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1#ifndef _theplu_yat_regression_local_
2#define _theplu_yat_regression_local_
3
4// \$Id: Local.h 2564 2011-09-25 20:03:41Z peter \$
5
6/*
7  Copyright (C) 2004 Peter Johansson
8  Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, Peter Johansson
9  Copyright (C) 2009, 2010, 2011 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
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 "yat/utility/Vector.h"
28
29#include <iosfwd>
30
31namespace theplu {
32namespace yat {
33namespace regression {
34
35  class Kernel;
36  class OneDimensionalWeighted;
37
38  ///
39  /// @brief Class for Locally weighted regression.
40  ///
41  /// Locally weighted regression is an algorithm for learning
42  /// continuous non-linear mappings in a non-parametric manner.  In
43  /// locally weighted regression, points are weighted by proximity to
44  /// the current x in question using a Kernel. A weighted regression
45  /// is then computed using the weighted points and a specific
46  /// Regression method. This procedure is repeated, which results in
47  /// a pointwise approximation of the underlying (unknown) function.
48  ///
49  class Local
50  {
51
52  public:
53    ///
54    /// @brief Constructor taking type of \a regressor,
55    /// type of \a kernel.
56    ///
57    Local(OneDimensionalWeighted& r, Kernel& k);
58
59    ///
60    /// @brief The destructor
61    ///
62    virtual ~Local(void);
63
64    ///
65    /// adding a data point
66    ///
67    void add(const double x, const double y);
68
69    ///
70    /// @param step_size Size of step between each fit
71    /// @param nof_points Number of points used in each fit
72    ///
73    /// \throw utility::runtime_error if step_size is 0, nof_points is
74    /// less than 3, or step_size is larger than number of added data
75    /// points.
76    void fit(const size_t step_size, const size_t nof_points);
77
78    /**
79       \brief Set everything to zero
80
81       \since New in yat 0.5
82     */
83    void reset(void);
84
85    ///
86    /// @return x-values where fitting was performed.
87    ///
88    const utility::Vector& x(void) const;
89
90    ///
91    /// Function returning predicted values
92    ///
93    const utility::Vector& y_predicted(void) const;
94
95    ///
96    /// Function returning error of predictions
97    ///
98    const utility::Vector& y_err(void) const;
99
100  private:
101    ///
102    /// Copy Constructor. (Not implemented)
103    ///
104    Local(const Local&);
105
106    std::vector<std::pair<double, double> > data_;
107    Kernel* kernel_;
108    OneDimensionalWeighted* regressor_;
109    utility::Vector x_;
110    utility::Vector y_predicted_;
111    utility::Vector y_err_;
112  };
113
114  ///
115  /// The output operator for the Regression::Local class.
116  ///
117  /// \relates Local
118  ///
119  std::ostream& operator<<(std::ostream&, const Local& );
120
121}}} // of namespaces regression, yat, and theplu
122
123#endif
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