1 | #ifndef _theplu_yat_classifier_knn_ |
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2 | #define _theplu_yat_classifier_knn_ |
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3 | |
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4 | // $Id: KNN.h 2334 2010-10-15 02:35:09Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2007, 2008 Jari Häkkinen, Peter Johansson, Markus Ringnér |
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8 | Copyright (C) 2009 Peter Johansson |
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9 | |
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10 | This file is part of the yat library, http://dev.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 3 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 yat. If not, see <http://www.gnu.org/licenses/>. |
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24 | */ |
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25 | |
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26 | #include "DataLookup1D.h" |
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27 | #include "DataLookupWeighted1D.h" |
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28 | #include "KNN_Uniform.h" |
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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 | #include "yat/utility/concept_check.h" |
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34 | #include "yat/utility/Exception.h" |
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35 | #include "yat/utility/Matrix.h" |
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36 | #include "yat/utility/yat_assert.h" |
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37 | |
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38 | #include <boost/concept_check.hpp> |
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39 | |
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40 | #include <cmath> |
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41 | #include <limits> |
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42 | #include <map> |
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43 | #include <stdexcept> |
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44 | |
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45 | namespace theplu { |
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46 | namespace yat { |
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47 | namespace classifier { |
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48 | |
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49 | /** |
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50 | \brief Nearest Neighbor Classifier |
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51 | |
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52 | A sample is predicted based on the classes of its k nearest |
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53 | neighbors among the training data samples. KNN supports using |
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54 | different measures, for example, Euclidean distance, to define |
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55 | distance between samples. KNN also supports using different ways to |
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56 | weight the votes of the k nearest neighbors. For example, using a |
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57 | uniform vote a test sample gets a vote for each class which is the |
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58 | number of nearest neighbors belonging to the class. |
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59 | |
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60 | The template argument Distance should be a class modelling the |
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61 | concept \ref concept_distance. The template argument |
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62 | NeighborWeighting should be a class modelling the concept \ref |
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63 | concept_neighbor_weighting. |
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64 | */ |
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65 | template <typename Distance, typename NeighborWeighting=KNN_Uniform> |
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66 | class KNN : public SupervisedClassifier |
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67 | { |
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68 | |
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69 | public: |
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70 | /** |
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71 | \brief Default constructor. |
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72 | |
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73 | The number of nearest neighbors (k) is set to 3. Distance and |
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74 | NeighborWeighting are initialized using their default |
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75 | constructuors. |
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76 | */ |
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77 | KNN(void); |
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78 | |
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79 | |
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80 | /** |
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81 | \brief Constructor using an intialized distance measure. |
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82 | |
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83 | The number of nearest neighbors (k) is set to |
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84 | 3. NeighborWeighting is initialized using its default |
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85 | constructor. This constructor should be used if Distance has |
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86 | parameters and the user wants to specify the parameters by |
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87 | initializing Distance prior to constructing the KNN. |
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88 | */ |
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89 | KNN(const Distance&); |
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90 | |
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91 | |
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92 | /** |
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93 | Destructor |
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94 | */ |
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95 | virtual ~KNN(); |
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96 | |
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97 | |
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98 | /** |
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99 | \brief Get the number of nearest neighbors. |
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100 | \return The number of neighbors. |
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101 | */ |
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102 | unsigned int k() const; |
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103 | |
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104 | /** |
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105 | \brief Set the number of nearest neighbors. |
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106 | |
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107 | Sets the number of neighbors to \a k_in. |
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108 | */ |
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109 | void k(unsigned int k_in); |
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110 | |
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111 | |
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112 | KNN<Distance,NeighborWeighting>* make_classifier(void) const; |
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113 | |
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114 | /** |
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115 | \brief Make predictions for unweighted test data. |
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116 | |
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117 | Predictions are calculated and returned in \a results. For |
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118 | each sample in \a data, \a results contains the weighted number |
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119 | of nearest neighbors which belong to each class. Numbers of |
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120 | nearest neighbors are weighted according to |
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121 | NeighborWeighting. If a class has no training samples NaN's are |
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122 | returned for this class in \a results. |
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123 | */ |
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124 | void predict(const MatrixLookup& data , utility::Matrix& results) const; |
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125 | |
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126 | /** |
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127 | \brief Make predictions for weighted test data. |
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128 | |
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129 | Predictions are calculated and returned in \a results. For |
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130 | each sample in \a data, \a results contains the weighted |
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131 | number of nearest neighbors which belong to each class as in |
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132 | predict(const MatrixLookup& data, utility::Matrix& results). |
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133 | If a test and training sample pair has no variables with |
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134 | non-zero weights in common, there are no variables which can |
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135 | be used to calculate the distance between the two samples. In |
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136 | this case the distance between the two is set to infinity. |
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137 | */ |
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138 | void predict(const MatrixLookupWeighted& data, utility::Matrix& results) const; |
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139 | |
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140 | |
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141 | /** |
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142 | \brief Train the KNN using unweighted training data with known |
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143 | targets. |
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144 | |
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145 | For KNN there is no actual training; the entire training data |
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146 | set is stored with targets. KNN only stores references to \a data |
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147 | and \a targets as copying these would make the %classifier |
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148 | slow. If the number of training samples set is smaller than k, |
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149 | k is set to the number of training samples. |
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150 | |
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151 | \note If \a data or \a targets go out of scope ore are |
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152 | deleted, the KNN becomes invalid and further use is undefined |
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153 | unless it is trained again. |
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154 | */ |
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155 | void train(const MatrixLookup& data, const Target& targets); |
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156 | |
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157 | /** |
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158 | \brief Train the KNN using weighted training data with known targets. |
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159 | |
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160 | See train(const MatrixLookup& data, const Target& targets) for |
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161 | additional information. |
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162 | */ |
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163 | void train(const MatrixLookupWeighted& data, const Target& targets); |
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164 | |
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165 | private: |
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166 | |
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167 | const MatrixLookup* data_ml_; |
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168 | const MatrixLookupWeighted* data_mlw_; |
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169 | const Target* target_; |
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170 | |
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171 | // The number of neighbors |
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172 | unsigned int k_; |
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173 | |
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174 | Distance distance_; |
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175 | NeighborWeighting weighting_; |
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176 | |
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177 | void calculate_unweighted(const MatrixLookup&, |
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178 | const MatrixLookup&, |
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179 | utility::Matrix*) const; |
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180 | void calculate_weighted(const MatrixLookupWeighted&, |
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181 | const MatrixLookupWeighted&, |
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182 | utility::Matrix*) const; |
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183 | |
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184 | void predict_common(const utility::Matrix& distances, |
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185 | utility::Matrix& prediction) const; |
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186 | |
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187 | }; |
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188 | |
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189 | |
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190 | // templates |
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191 | |
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192 | template <typename Distance, typename NeighborWeighting> |
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193 | KNN<Distance, NeighborWeighting>::KNN() |
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194 | : SupervisedClassifier(),data_ml_(0),data_mlw_(0),target_(0),k_(3) |
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195 | { |
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196 | BOOST_CONCEPT_ASSERT((utility::DistanceConcept<Distance>)); |
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197 | } |
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198 | |
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199 | template <typename Distance, typename NeighborWeighting> |
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200 | KNN<Distance, NeighborWeighting>::KNN(const Distance& dist) |
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201 | : SupervisedClassifier(),data_ml_(0),data_mlw_(0),target_(0),k_(3), distance_(dist) |
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202 | { |
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203 | BOOST_CONCEPT_ASSERT((utility::DistanceConcept<Distance>)); |
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204 | } |
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205 | |
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206 | |
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207 | template <typename Distance, typename NeighborWeighting> |
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208 | KNN<Distance, NeighborWeighting>::~KNN() |
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209 | { |
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210 | } |
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211 | |
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212 | |
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213 | template <typename Distance, typename NeighborWeighting> |
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214 | void KNN<Distance, NeighborWeighting>::calculate_unweighted |
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215 | (const MatrixLookup& training, const MatrixLookup& test, |
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216 | utility::Matrix* distances) const |
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217 | { |
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218 | for(size_t i=0; i<training.columns(); i++) { |
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219 | for(size_t j=0; j<test.columns(); j++) { |
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220 | (*distances)(i,j) = distance_(training.begin_column(i), training.end_column(i), |
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221 | test.begin_column(j)); |
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222 | YAT_ASSERT(!std::isnan((*distances)(i,j))); |
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223 | } |
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224 | } |
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225 | } |
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226 | |
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227 | |
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228 | template <typename Distance, typename NeighborWeighting> |
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229 | void |
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230 | KNN<Distance, NeighborWeighting>::calculate_weighted |
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231 | (const MatrixLookupWeighted& training, const MatrixLookupWeighted& test, |
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232 | utility::Matrix* distances) const |
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233 | { |
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234 | for(size_t i=0; i<training.columns(); i++) { |
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235 | for(size_t j=0; j<test.columns(); j++) { |
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236 | (*distances)(i,j) = distance_(training.begin_column(i), training.end_column(i), |
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237 | test.begin_column(j)); |
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238 | // If the distance is NaN (no common variables with non-zero weights), |
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239 | // the distance is set to infinity to be sorted as a neighbor at the end |
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240 | if(std::isnan((*distances)(i,j))) |
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241 | (*distances)(i,j)=std::numeric_limits<double>::infinity(); |
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242 | } |
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243 | } |
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244 | } |
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245 | |
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246 | |
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247 | template <typename Distance, typename NeighborWeighting> |
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248 | unsigned int KNN<Distance, NeighborWeighting>::k() const |
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249 | { |
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250 | return k_; |
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251 | } |
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252 | |
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253 | template <typename Distance, typename NeighborWeighting> |
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254 | void KNN<Distance, NeighborWeighting>::k(unsigned int k) |
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255 | { |
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256 | k_=k; |
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257 | } |
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258 | |
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259 | |
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260 | template <typename Distance, typename NeighborWeighting> |
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261 | KNN<Distance, NeighborWeighting>* |
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262 | KNN<Distance, NeighborWeighting>::make_classifier() const |
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263 | { |
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264 | // All private members should be copied here to generate an |
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265 | // identical but untrained classifier |
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266 | KNN* knn=new KNN<Distance, NeighborWeighting>(distance_); |
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267 | knn->weighting_=this->weighting_; |
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268 | knn->k(this->k()); |
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269 | return knn; |
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270 | } |
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271 | |
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272 | |
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273 | template <typename Distance, typename NeighborWeighting> |
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274 | void KNN<Distance, NeighborWeighting>::train(const MatrixLookup& data, |
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275 | const Target& target) |
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276 | { |
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277 | utility::yat_assert<utility::runtime_error> |
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278 | (data.columns()==target.size(), |
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279 | "KNN::train called with different sizes of target and data"); |
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280 | // k has to be at most the number of training samples. |
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281 | if(data.columns()<k_) |
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282 | k_=data.columns(); |
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283 | data_ml_=&data; |
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284 | data_mlw_=0; |
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285 | target_=⌖ |
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286 | } |
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287 | |
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288 | template <typename Distance, typename NeighborWeighting> |
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289 | void KNN<Distance, NeighborWeighting>::train(const MatrixLookupWeighted& data, |
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290 | const Target& target) |
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291 | { |
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292 | utility::yat_assert<utility::runtime_error> |
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293 | (data.columns()==target.size(), |
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294 | "KNN::train called with different sizes of target and data"); |
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295 | // k has to be at most the number of training samples. |
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296 | if(data.columns()<k_) |
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297 | k_=data.columns(); |
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298 | data_ml_=0; |
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299 | data_mlw_=&data; |
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300 | target_=⌖ |
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301 | } |
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302 | |
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303 | |
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304 | template <typename Distance, typename NeighborWeighting> |
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305 | void KNN<Distance, NeighborWeighting>::predict(const MatrixLookup& test, |
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306 | utility::Matrix& prediction) const |
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307 | { |
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308 | // matrix with training samples as rows and test samples as columns |
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309 | utility::Matrix* distances = 0; |
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310 | // unweighted training data |
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311 | if(data_ml_ && !data_mlw_) { |
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312 | utility::yat_assert<utility::runtime_error> |
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313 | (data_ml_->rows()==test.rows(), |
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314 | "KNN::predict different number of rows in training and test data"); |
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315 | distances=new utility::Matrix(data_ml_->columns(),test.columns()); |
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316 | calculate_unweighted(*data_ml_,test,distances); |
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317 | } |
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318 | else if (data_mlw_ && !data_ml_) { |
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319 | // weighted training data |
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320 | utility::yat_assert<utility::runtime_error> |
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321 | (data_mlw_->rows()==test.rows(), |
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322 | "KNN::predict different number of rows in training and test data"); |
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323 | distances=new utility::Matrix(data_mlw_->columns(),test.columns()); |
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324 | calculate_weighted(*data_mlw_,MatrixLookupWeighted(test), |
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325 | distances); |
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326 | } |
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327 | else { |
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328 | throw utility::runtime_error("KNN::predict no training data"); |
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329 | } |
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330 | |
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331 | prediction.resize(target_->nof_classes(),test.columns(),0.0); |
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332 | predict_common(*distances,prediction); |
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333 | if(distances) |
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334 | delete distances; |
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335 | } |
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336 | |
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337 | template <typename Distance, typename NeighborWeighting> |
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338 | void KNN<Distance, NeighborWeighting>::predict(const MatrixLookupWeighted& test, |
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339 | utility::Matrix& prediction) const |
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340 | { |
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341 | // matrix with training samples as rows and test samples as columns |
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342 | utility::Matrix* distances=0; |
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343 | // unweighted training data |
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344 | if(data_ml_ && !data_mlw_) { |
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345 | utility::yat_assert<utility::runtime_error> |
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346 | (data_ml_->rows()==test.rows(), |
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347 | "KNN::predict different number of rows in training and test data"); |
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348 | distances=new utility::Matrix(data_ml_->columns(),test.columns()); |
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349 | calculate_weighted(MatrixLookupWeighted(*data_ml_),test,distances); |
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350 | } |
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351 | // weighted training data |
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352 | else if (data_mlw_ && !data_ml_) { |
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353 | utility::yat_assert<utility::runtime_error> |
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354 | (data_mlw_->rows()==test.rows(), |
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355 | "KNN::predict different number of rows in training and test data"); |
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356 | distances=new utility::Matrix(data_mlw_->columns(),test.columns()); |
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357 | calculate_weighted(*data_mlw_,test,distances); |
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358 | } |
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359 | else { |
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360 | throw utility::runtime_error("KNN::predict no training data"); |
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361 | } |
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362 | |
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363 | prediction.resize(target_->nof_classes(),test.columns(),0.0); |
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364 | predict_common(*distances,prediction); |
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365 | |
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366 | if(distances) |
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367 | delete distances; |
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368 | } |
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369 | |
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370 | template <typename Distance, typename NeighborWeighting> |
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371 | void KNN<Distance, NeighborWeighting>::predict_common |
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372 | (const utility::Matrix& distances, utility::Matrix& prediction) const |
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373 | { |
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374 | for(size_t sample=0;sample<distances.columns();sample++) { |
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375 | std::vector<size_t> k_index; |
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376 | utility::VectorConstView dist=distances.column_const_view(sample); |
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377 | utility::sort_smallest_index(k_index,k_,dist); |
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378 | utility::VectorView pred=prediction.column_view(sample); |
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379 | weighting_(dist,k_index,*target_,pred); |
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380 | } |
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381 | |
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382 | // classes for which there are no training samples should be set |
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383 | // to nan in the predictions |
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384 | for(size_t c=0;c<target_->nof_classes(); c++) |
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385 | if(!target_->size(c)) |
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386 | for(size_t j=0;j<prediction.columns();j++) |
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387 | prediction(c,j)=std::numeric_limits<double>::quiet_NaN(); |
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388 | } |
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389 | |
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390 | |
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391 | }}} // of namespace classifier, yat, and theplu |
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392 | |
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393 | #endif |
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394 | |
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