1 | #ifndef _theplu_yat_classifier_ncc_ |
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2 | #define _theplu_yat_classifier_ncc_ |
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3 | |
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4 | // $Id$ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2005 Markus Ringnér, Peter Johansson |
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8 | Copyright (C) 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson |
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9 | Copyright (C) 2007 Peter Johansson |
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10 | |
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11 | This file is part of the yat library, http://trac.thep.lu.se/yat |
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12 | |
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13 | The yat library is free software; you can redistribute it and/or |
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14 | modify it under the terms of the GNU General Public License as |
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15 | published by the Free Software Foundation; either version 2 of the |
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16 | License, or (at your option) any later version. |
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17 | |
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18 | The yat library is distributed in the hope that it will be useful, |
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19 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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20 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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21 | General Public License for more details. |
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22 | |
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23 | You should have received a copy of the GNU General Public License |
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24 | along with this program; if not, write to the Free Software |
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25 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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26 | 02111-1307, USA. |
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27 | */ |
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28 | |
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29 | #include "MatrixLookup.h" |
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30 | #include "MatrixLookupWeighted.h" |
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31 | #include "SupervisedClassifier.h" |
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32 | #include "Target.h" |
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33 | |
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34 | #include "yat/statistics/Averager.h" |
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35 | #include "yat/statistics/AveragerWeighted.h" |
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36 | #include "yat/utility/Matrix.h" |
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37 | #include "yat/utility/Vector.h" |
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38 | #include "yat/utility/stl_utility.h" |
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39 | #include "yat/utility/yat_assert.h" |
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40 | |
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41 | #include<iostream> |
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42 | #include<iterator> |
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43 | #include <map> |
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44 | #include <cmath> |
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45 | #include <stdexcept> |
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46 | |
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47 | namespace theplu { |
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48 | namespace yat { |
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49 | namespace classifier { |
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50 | |
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51 | |
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52 | /** |
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53 | \brief Nearest Centroid Classifier |
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54 | |
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55 | A sample is predicted based on its distance to centroids for each |
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56 | class. The centroids are generated using training data. NCC |
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57 | supports using different measures, for example, Euclidean |
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58 | distance, to define distance between samples and centroids. |
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59 | |
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60 | The template argument Distance should be a class modelling |
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61 | the concept \ref concept_distance. |
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62 | */ |
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63 | template <typename Distance> |
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64 | class NCC : public SupervisedClassifier |
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65 | { |
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66 | |
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67 | public: |
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68 | /** |
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69 | \brief Constructor |
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70 | |
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71 | Distance is initialized using its default constructor. |
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72 | */ |
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73 | NCC(void); |
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74 | |
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75 | /** |
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76 | \brief Constructor using an initialized distance measure |
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77 | |
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78 | This constructor should be used if Distance has parameters and |
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79 | the user wants to specify the parameters by initializing |
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80 | Distance prior to constructing the NCC. |
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81 | */ |
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82 | NCC(const Distance&); |
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83 | |
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84 | |
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85 | /** |
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86 | Destructor |
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87 | */ |
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88 | virtual ~NCC(void); |
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89 | |
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90 | /** |
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91 | \brief Get the centroids for all classes. |
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92 | |
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93 | \return The centroids for each class as columns in a matrix. |
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94 | */ |
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95 | const utility::Matrix& centroids(void) const; |
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96 | |
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97 | NCC<Distance>* make_classifier(void) const; |
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98 | |
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99 | /** |
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100 | \brief Make predictions for unweighted test data. |
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101 | |
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102 | Predictions are calculated and returned in \a results. For |
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103 | each sample in \a data, \a results contains the distances to |
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104 | the centroids for each class. If a class has no training |
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105 | samples NaN's are returned for this class in \a |
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106 | results. Weighted distance calculations, in which NaN's have |
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107 | zero weights, are used if the centroids contain NaN's. |
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108 | |
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109 | \note NCC returns distances to centroids as the |
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110 | prediction. This means that the best class for a sample has the |
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111 | smallest value in \a results. This is in contrast to, for |
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112 | example, KNN for which the best class for a sample in \a |
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113 | results has the largest number (the largest number of nearest |
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114 | neighbors). |
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115 | */ |
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116 | void predict(const MatrixLookup& data, utility::Matrix& results) const; |
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117 | |
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118 | /** |
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119 | \brief Make predictions for weighted test data. |
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120 | |
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121 | Predictions are calculated and returned in \a results. For |
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122 | each sample in \a data, \a results contains the distances to |
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123 | the centroids for each class as in predict(const MatrixLookup& |
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124 | data, utility::Matrix& results). Weighted distance calculations |
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125 | are used, and zero weights are used for NaN's in centroids. If |
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126 | for a test sample and centroid pair, all variables have either |
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127 | zero weight for the test sample or NaN for the centroid, the |
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128 | centroid and the sample have no variables with values in |
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129 | common. In this case the prediction for the sample is set to |
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130 | NaN for the class in \a results. |
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131 | |
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132 | \note NCC returns distances to centroids as the |
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133 | prediction. This means that the best class for a sample has the |
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134 | smallest value in \a results. This is in contrast to, for |
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135 | example, KNN for which the best class for a sample in \a |
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136 | results has the largest number (the largest number of nearest |
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137 | neighbors). |
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138 | */ |
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139 | void predict(const MatrixLookupWeighted& data, utility::Matrix& results) const; |
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140 | |
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141 | /** |
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142 | \brief Train the NCC using unweighted training data with known |
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143 | targets. |
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144 | |
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145 | A centroid is calculated for each class. For each variable in |
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146 | \a data, a centroid for a class contains the average value of |
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147 | the variable across all training samples in the class. |
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148 | */ |
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149 | void train(const MatrixLookup& data, const Target& targets); |
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150 | |
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151 | |
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152 | /** |
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153 | \brief Train the NCC using weighted training data with known |
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154 | targets. |
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155 | |
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156 | A centroid is calculated for each class as in |
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157 | train(const MatrixLookup&, const Target&). |
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158 | The weights of the data are used when calculating the centroids |
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159 | and the centroids should be interpreted as unweighted |
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160 | (i.e. centroid values have unity weights). If a variable has |
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161 | zero weights for all samples in a class, the centroid is set to |
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162 | NaN for that variable. |
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163 | */ |
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164 | void train(const MatrixLookupWeighted& data, const Target& targets); |
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165 | |
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166 | |
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167 | private: |
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168 | |
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169 | void predict_unweighted(const MatrixLookup&, utility::Matrix&) const; |
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170 | void predict_weighted(const MatrixLookupWeighted&, utility::Matrix&) const; |
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171 | |
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172 | utility::Matrix centroids_; |
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173 | bool centroids_nan_; |
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174 | Distance distance_; |
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175 | }; |
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176 | |
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177 | // templates |
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178 | |
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179 | template <typename Distance> |
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180 | NCC<Distance>::NCC() |
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181 | : SupervisedClassifier(), centroids_nan_(false) |
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182 | { |
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183 | } |
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184 | |
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185 | template <typename Distance> |
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186 | NCC<Distance>::NCC(const Distance& dist) |
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187 | : SupervisedClassifier(), centroids_nan_(false), distance_(dist) |
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188 | { |
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189 | } |
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190 | |
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191 | |
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192 | template <typename Distance> |
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193 | NCC<Distance>::~NCC() |
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194 | { |
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195 | } |
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196 | |
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197 | |
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198 | template <typename Distance> |
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199 | const utility::Matrix& NCC<Distance>::centroids(void) const |
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200 | { |
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201 | return centroids_; |
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202 | } |
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203 | |
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204 | |
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205 | template <typename Distance> |
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206 | NCC<Distance>* |
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207 | NCC<Distance>::make_classifier() const |
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208 | { |
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209 | // All private members should be copied here to generate an |
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210 | // identical but untrained classifier |
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211 | return new NCC<Distance>(distance_); |
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212 | } |
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213 | |
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214 | template <typename Distance> |
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215 | void NCC<Distance>::train(const MatrixLookup& data, const Target& target) |
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216 | { |
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217 | centroids_.resize(data.rows(), target.nof_classes()); |
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218 | for(size_t i=0; i<data.rows(); i++) { |
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219 | std::vector<statistics::Averager> class_averager; |
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220 | class_averager.resize(target.nof_classes()); |
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221 | for(size_t j=0; j<data.columns(); j++) { |
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222 | class_averager[target(j)].add(data(i,j)); |
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223 | } |
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224 | for(size_t c=0;c<target.nof_classes();c++) { |
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225 | centroids_(i,c) = class_averager[c].mean(); |
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226 | } |
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227 | } |
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228 | } |
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229 | |
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230 | |
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231 | template <typename Distance> |
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232 | void NCC<Distance>::train(const MatrixLookupWeighted& data, const Target& target) |
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233 | { |
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234 | centroids_.resize(data.rows(), target.nof_classes()); |
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235 | for(size_t i=0; i<data.rows(); i++) { |
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236 | std::vector<statistics::AveragerWeighted> class_averager; |
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237 | class_averager.resize(target.nof_classes()); |
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238 | for(size_t j=0; j<data.columns(); j++) |
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239 | class_averager[target(j)].add(data.data(i,j),data.weight(i,j)); |
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240 | for(size_t c=0;c<target.nof_classes();c++) { |
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241 | if(class_averager[c].sum_w()==0) { |
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242 | centroids_nan_=true; |
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243 | } |
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244 | centroids_(i,c) = class_averager[c].mean(); |
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245 | } |
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246 | } |
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247 | } |
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248 | |
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249 | |
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250 | template <typename Distance> |
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251 | void NCC<Distance>::predict(const MatrixLookup& test, |
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252 | utility::Matrix& prediction) const |
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253 | { |
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254 | utility::yat_assert<std::runtime_error> |
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255 | (centroids_.rows()==test.rows(), |
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256 | "NCC::predict test data with incorrect number of rows"); |
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257 | |
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258 | prediction.resize(centroids_.columns(), test.columns()); |
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259 | |
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260 | // If weighted training data has resulted in NaN in centroids: weighted calculations |
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261 | if(centroids_nan_) { |
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262 | predict_weighted(MatrixLookupWeighted(test),prediction); |
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263 | } |
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264 | // If unweighted training data: unweighted calculations |
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265 | else { |
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266 | predict_unweighted(test,prediction); |
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267 | } |
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268 | } |
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269 | |
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270 | template <typename Distance> |
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271 | void NCC<Distance>::predict(const MatrixLookupWeighted& test, |
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272 | utility::Matrix& prediction) const |
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273 | { |
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274 | utility::yat_assert<std::runtime_error> |
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275 | (centroids_.rows()==test.rows(), |
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276 | "NCC::predict test data with incorrect number of rows"); |
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277 | |
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278 | prediction.resize(centroids_.columns(), test.columns()); |
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279 | predict_weighted(test,prediction); |
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280 | } |
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281 | |
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282 | |
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283 | template <typename Distance> |
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284 | void NCC<Distance>::predict_unweighted(const MatrixLookup& test, |
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285 | utility::Matrix& prediction) const |
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286 | { |
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287 | for(size_t j=0; j<test.columns();j++) |
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288 | for(size_t k=0; k<centroids_.columns();k++) |
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289 | prediction(k,j) = distance_(test.begin_column(j), test.end_column(j), |
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290 | centroids_.begin_column(k)); |
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291 | } |
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292 | |
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293 | template <typename Distance> |
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294 | void NCC<Distance>::predict_weighted(const MatrixLookupWeighted& test, |
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295 | utility::Matrix& prediction) const |
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296 | { |
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297 | MatrixLookupWeighted weighted_centroids(centroids_); |
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298 | for(size_t j=0; j<test.columns();j++) |
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299 | for(size_t k=0; k<centroids_.columns();k++) |
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300 | prediction(k,j) = distance_(test.begin_column(j), test.end_column(j), |
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301 | weighted_centroids.begin_column(k)); |
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302 | } |
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303 | |
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304 | |
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305 | }}} // of namespace classifier, yat, and theplu |
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306 | |
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307 | #endif |
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