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 1182 2008-02-28 12:27:37Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson |
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8 | Copyright (C) 2007 Peter Johansson |
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9 | |
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10 | This file is part of the yat library, http://trac.thep.lu.se/yat |
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11 | |
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12 | The yat library is free software; you can redistribute it and/or |
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13 | modify it under the terms of the GNU General Public License as |
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14 | published by the Free Software Foundation; either version 2 of the |
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15 | License, or (at your option) any later version. |
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16 | |
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17 | The yat library is distributed in the hope that it will be useful, |
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18 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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19 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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20 | General Public License for more details. |
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21 | |
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22 | You should have received a copy of the GNU General Public License |
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23 | along with this program; if not, write to the Free Software |
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24 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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25 | 02111-1307, USA. |
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26 | */ |
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27 | |
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28 | #include "SupervisedClassifier.h" |
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29 | #include "yat/utility/Matrix.h" |
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30 | |
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31 | namespace theplu { |
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32 | namespace yat { |
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33 | namespace classifier { |
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34 | |
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35 | class MatrixLookup; |
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36 | class MatrixLookupWeighted; |
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37 | class Target; |
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38 | |
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39 | /** |
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40 | @brief Naive Bayesian Classifier. |
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41 | |
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42 | Each class is modelled as a multinormal distribution with |
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43 | features being independent: \f$ p(x|c) = \prod |
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44 | \frac{1}{\sqrt{2\pi\sigma_i^2}} \exp \left( |
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45 | \frac{(x_i-m_i)^2}{2\sigma_i^2)} \right)\f$ |
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46 | */ |
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47 | class NBC : public SupervisedClassifier |
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48 | { |
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49 | |
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50 | public: |
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51 | /// |
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52 | /// @brief Constructor |
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53 | /// |
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54 | NBC(void); |
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55 | |
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56 | |
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57 | /// |
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58 | /// @brief Destructor |
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59 | /// |
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60 | virtual ~NBC(); |
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61 | |
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62 | |
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63 | NBC* make_classifier(void) const; |
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64 | |
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65 | /// |
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66 | /// Train the classifier using training data and targets. |
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67 | /// |
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68 | /// For each class mean and variance are estimated for each |
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69 | /// feature (see Averager and AveragerWeighted for details). |
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70 | /// |
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71 | /// If variance can not be estimated (only one valid data point) |
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72 | /// for a feature and label, then that feature is ignored for that |
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73 | /// specific label. |
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74 | /// |
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75 | void train(const MatrixLookup&, const Target&); |
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76 | |
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77 | /// |
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78 | /// Train the classifier using weighted training data and targets. |
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79 | /// |
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80 | void train(const MatrixLookupWeighted&, const Target&); |
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81 | |
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82 | |
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83 | |
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84 | /** |
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85 | Each sample (column) in \a data is predicted and predictions |
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86 | are returned in the corresponding column in passed \a res. Each |
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87 | row in \a res corresponds to a class. The prediction is the |
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88 | estimated probability that sample belong to class \f$ j \f$ |
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89 | |
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90 | \f$ P_j = \frac{1}{Z}\prod_i{\frac{1}{\sqrt{2\pi\sigma_i^2}}} |
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91 | \exp(\frac{(x_i-\mu_i)^2}{\sigma_i^2})\f$, where \f$ \mu_i |
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92 | \f$ and \f$ \sigma_i^2 \f$ are the estimated mean and variance, |
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93 | respectively. If a \f$ \sigma_i \f$ could not be estimated |
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94 | during training, corresponding factor is set to unity, in other |
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95 | words, that feature is ignored for the prediction of that |
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96 | particular class. Z is chosen such that total probability, \f$ |
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97 | \sum P_j \f$, equals unity. |
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98 | */ |
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99 | void predict(const MatrixLookup& data, utility::Matrix& res) const; |
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100 | |
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101 | /** |
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102 | Each sample (column) in \a data is predicted and predictions |
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103 | are returned in the corresponding column in passed \a res. Each |
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104 | row in \a res corresponds to a class. The prediction is the |
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105 | estimated probability that sample belong to class \f$ j \f$ |
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106 | |
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107 | \f$ P_j = \frac{1}{Z}\prod_i\({\frac{1}{\sqrt{2\pi\sigma_i^2}}}\) |
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108 | \exp(\frac{\sum{w_i(x_i-\mu_i)^2}{\sigma_i^2}}{\sum w_i})\f$, |
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109 | where \f$ \mu_i |
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110 | \f$ and \f$ \sigma_i^2 \f$ are the estimated mean and variance, |
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111 | respectively. If a \f$ \sigma_i \f$ could not be estimated |
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112 | during training, corresponding factor is set to unity, in other |
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113 | words, that feature is ignored for the prediction of that |
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114 | particular class. Z is chosen such that total probability, \f$ |
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115 | \sum P_j \f$, equals unity. |
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116 | */ |
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117 | void predict(const MatrixLookupWeighted& data, utility::Matrix& res) const; |
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118 | |
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119 | |
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120 | private: |
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121 | void standardize_lnP(utility::Matrix& prediction) const; |
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122 | |
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123 | utility::Matrix centroids_; |
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124 | utility::Matrix sigma2_; |
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125 | |
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126 | double sum_logsigma(size_t i) const; |
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127 | |
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128 | |
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129 | }; |
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130 | |
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131 | }}} // of namespace classifier, yat, and theplu |
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132 | |
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133 | #endif |
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