source: trunk/yat/classifier/NBC.h @ 813

Last change on this file since 813 was 813, checked in by Peter, 15 years ago

Predict in NBC. Fixes #57

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  • Property svn:keywords set to Id
File size: 3.0 KB
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1#ifndef _theplu_yat_classifier_nbc_
2#define _theplu_yat_classifier_nbc_
3
4// $Id: NBC.h 813 2007-03-16 19:30:02Z 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       Each sample (column) in \a data is predicted and predictions
86       are returned in the corresponding column in passed \a res. Each
87       row in \a res corresponds to a class. The prediction is the
88       estimated probability that sample belong to class \f$ j \f$
89
90       \f$ P_j = \frac{1}{Z}\prod_i{\frac{1}{\sigma_i}}
91       \exp(\frac{w_i(x_i-\mu_i)^2}{\sigma_i^2})\f$, where \f$ \mu_i
92       \f$ and \f$ \sigma_i^2 \f$ are the estimated mean and variance,
93       respectively. If \a data is a MatrixLookup is equivalent to
94       using all weight equal to unity.
95    */
96    void predict(const DataLookup2D& data, utility::matrix& res) const;
97
98
99  private:
100    utility::matrix centroids_;
101    utility::matrix sigma2_;
102    const DataLookup2D& data_;
103
104  };
105 
106}}} // of namespace classifier, yat, and theplu
107
108#endif
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