1 | #ifndef _theplu_yat_classifier_nbc_ |
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2 | #define _theplu_yat_classifier_nbc_ |
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3 | |
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4 | // $Id: NBC.h 808 2007-03-15 20:07:01Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) The authors contributing to this file. |
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8 | |
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9 | This file is part of the yat library, http://lev.thep.lu.se/trac/yat |
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10 | |
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11 | The yat library is free software; you can redistribute it and/or |
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12 | modify it under the terms of the GNU General Public License as |
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13 | published by the Free Software Foundation; either version 2 of the |
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14 | License, or (at your option) any later version. |
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15 | |
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16 | The yat library is distributed in the hope that it will be useful, |
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17 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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18 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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19 | General Public License for more details. |
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20 | |
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21 | You should have received a copy of the GNU General Public License |
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22 | along with this program; if not, write to the Free Software |
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23 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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24 | 02111-1307, USA. |
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25 | */ |
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26 | |
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27 | #include "SupervisedClassifier.h" |
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28 | #include "yat/utility/matrix.h" |
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29 | |
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30 | namespace theplu { |
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31 | namespace yat { |
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32 | namespace classifier { |
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33 | |
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34 | class DataLookup1D; |
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35 | class DataLookup2D; |
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36 | class MatrixLookup; |
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37 | class MatrixLookupWeighted; |
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38 | class Target; |
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39 | |
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40 | /** |
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41 | @brief Naive Bayesian Classification. |
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42 | |
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43 | Each class is modelled as a multinormal distribution with |
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44 | features being independent: \f$ p(x|c) = \prod |
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45 | \frac{1}{\sqrt{2\pi\sigma_i^2}} \exp \left( |
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46 | \frac{(x_i-m_i)^2}{2\sigma_i^2)} \right)\f$ |
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47 | */ |
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48 | class NBC : public SupervisedClassifier |
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49 | { |
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50 | |
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51 | public: |
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52 | /// |
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53 | /// Constructor taking the training data, the target vector, and |
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54 | /// the distance measure as input. |
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55 | /// |
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56 | NBC(const MatrixLookup&, const Target&); |
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57 | |
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58 | /// |
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59 | /// Constructor taking the training data with weights, the target |
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60 | /// vector, the distance measure, and a weight matrix for the |
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61 | /// training data as input. |
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62 | /// |
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63 | NBC(const MatrixLookupWeighted&, const Target&); |
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64 | |
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65 | virtual ~NBC(); |
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66 | |
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67 | const DataLookup2D& data(void) const; |
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68 | |
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69 | |
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70 | SupervisedClassifier* make_classifier(const DataLookup2D&, |
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71 | const Target&) const; |
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72 | |
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73 | /// |
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74 | /// Train the classifier using the training data. |
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75 | /// |
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76 | /// For each class mean and variance are estimated for each |
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77 | /// feature (see Averager and AveragerWeighted for details). |
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78 | /// |
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79 | /// @return true if training succedeed. |
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80 | /// |
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81 | bool train(); |
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82 | |
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83 | |
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84 | /** |
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85 | For each sample, calculate the probabilities the sample belong |
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86 | to the corresponding class. |
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87 | */ |
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88 | void predict(const DataLookup2D& data, utility::matrix& res) const; |
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89 | |
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90 | |
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91 | private: |
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92 | double gaussian(double x, double m, double sigma) const; |
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93 | |
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94 | utility::matrix centroids_; |
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95 | utility::matrix sigma2_; |
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96 | const DataLookup2D& data_; |
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97 | |
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98 | }; |
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99 | |
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100 | }}} // of namespace classifier, yat, and theplu |
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101 | |
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102 | #endif |
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