source: trunk/yat/classifier/NBC.h @ 812

Last change on this file since 812 was 812, checked in by Peter, 15 years ago

implemented NBC predict. Have to check what happens in the weighted case though, Refs #57

  • Property svn:eol-style set to native
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File size: 2.6 KB
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1#ifndef _theplu_yat_classifier_nbc_
2#define _theplu_yat_classifier_nbc_
3
4// $Id: NBC.h 812 2007-03-16 01:02:07Z 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 "SupervisedClassifier.h"
28#include "yat/utility/matrix.h"
29
30namespace theplu {
31namespace yat {
32namespace classifier { 
33
34  class DataLookup1D;
35  class DataLookup2D;
36  class MatrixLookup;
37  class MatrixLookupWeighted;
38  class Target;
39
40  /**
41     @brief Naive Bayesian Classification.
42 
43     Each class is modelled as a multinormal distribution with
44     features being independent: \f$ p(x|c) = \prod
45     \frac{1}{\sqrt{2\pi\sigma_i^2}} \exp \left(
46     \frac{(x_i-m_i)^2}{2\sigma_i^2)} \right)\f$
47  */
48  class NBC : public SupervisedClassifier
49  {
50 
51  public:
52    ///
53    /// Constructor taking the training data, the target vector, and
54    /// the distance measure as input.
55    ///
56    NBC(const MatrixLookup&, const Target&);
57   
58    ///
59    /// Constructor taking the training data with weights, the target
60    /// vector, the distance measure, and a weight matrix for the
61    /// training data as input.
62    ///
63    NBC(const MatrixLookupWeighted&, const Target&);
64
65    virtual ~NBC();
66
67    const DataLookup2D& data(void) const;
68
69
70    SupervisedClassifier* make_classifier(const DataLookup2D&, 
71                                          const Target&) const;
72   
73    ///
74    /// Train the classifier using the training data.
75    ///
76    /// For each class mean and variance are estimated for each
77    /// feature (see Averager and AveragerWeighted for details).
78    ///
79    /// @return true if training succedeed.
80    ///
81    bool train();
82
83   
84    /**
85       For each sample, calculate the probabilities the sample belong
86       to the corresponding class.
87    */
88    void predict(const DataLookup2D& data, utility::matrix& res) const;
89
90
91  private:
92    utility::matrix centroids_;
93    utility::matrix sigma2_;
94    const DataLookup2D& data_;
95
96  };
97 
98}}} // of namespace classifier, yat, and theplu
99
100#endif
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