1 | #ifndef _theplu_yat_classifier_ensemblebuilder_ |
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2 | #define _theplu_yat_classifier_ensemblebuilder_ |
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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 |
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8 | Copyright (C) 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson |
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9 | Copyright (C) 2007, 2008 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 "FeatureSelector.h" |
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30 | #include "Sampler.h" |
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31 | #include "SubsetGenerator.h" |
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32 | #include "yat/statistics/Averager.h" |
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33 | |
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34 | #include <vector> |
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35 | |
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36 | namespace theplu { |
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37 | namespace yat { |
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38 | namespace classifier { |
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39 | |
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40 | /// |
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41 | /// @brief Class for ensembles of supervised classifiers |
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42 | /// |
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43 | template <class Classifier, class Data> |
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44 | class EnsembleBuilder |
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45 | { |
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46 | |
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47 | public: |
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48 | typedef Classifier classifier_type; |
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49 | typedef Data data_type; |
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50 | |
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51 | /// |
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52 | /// Constructor. |
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53 | /// |
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54 | EnsembleBuilder(const Classifier&, const Data&, const Sampler&); |
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55 | |
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56 | /// |
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57 | /// Constructor. |
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58 | /// |
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59 | EnsembleBuilder(const Classifier&, const Data&, const Sampler&, |
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60 | FeatureSelector&); |
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61 | |
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62 | /// |
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63 | /// Destructor. |
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64 | /// |
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65 | virtual ~EnsembleBuilder(void); |
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66 | |
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67 | /// |
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68 | /// Generate ensemble. Function trains each member of the Ensemble. |
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69 | /// |
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70 | void build(void); |
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71 | |
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72 | /// |
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73 | /// @Return classifier |
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74 | /// |
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75 | const Classifier& classifier(size_t i) const; |
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76 | |
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77 | /// |
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78 | /// @Return Number of classifiers in ensemble |
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79 | /// |
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80 | u_long size(void) const; |
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81 | |
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82 | /// |
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83 | /// @brief Generate validation data for ensemble |
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84 | /// |
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85 | /// validate()[i][j] return averager for class @a i for sample @a j |
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86 | /// |
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87 | const std::vector<std::vector<statistics::Averager> >& validate(void); |
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88 | |
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89 | /** |
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90 | Predict a dataset using the ensemble. |
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91 | |
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92 | If @a data is a KernelLookup each column should correspond to a |
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93 | test sample and each row should correspond to a training |
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94 | sample. More exactly row \f$ i \f$ in @a data should correspond |
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95 | to the same sample as row/column \f$ i \f$ in the training |
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96 | kernel corresponds to. |
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97 | */ |
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98 | void predict(const Data& data, |
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99 | std::vector<std::vector<statistics::Averager> > &); |
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100 | |
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101 | private: |
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102 | // no copying |
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103 | EnsembleBuilder(const EnsembleBuilder&); |
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104 | const EnsembleBuilder& operator=(const EnsembleBuilder&); |
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105 | |
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106 | |
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107 | const Classifier& mother_; |
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108 | SubsetGenerator<Data>* subset_; |
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109 | std::vector<Classifier*> classifier_; |
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110 | std::vector<std::vector<statistics::Averager> > validation_result_; |
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111 | |
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112 | }; |
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113 | |
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114 | |
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115 | // implementation |
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116 | |
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117 | template <class C, class D> |
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118 | EnsembleBuilder<C,D>::EnsembleBuilder(const C& sc, const D& data, |
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119 | const Sampler& sampler) |
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120 | : mother_(sc),subset_(new SubsetGenerator<D>(sampler,data)) |
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121 | { |
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122 | } |
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123 | |
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124 | |
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125 | template <class C, class D> |
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126 | EnsembleBuilder<C, D>::EnsembleBuilder(const C& sc, const D& data, |
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127 | const Sampler& sampler, |
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128 | FeatureSelector& fs) |
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129 | : mother_(sc), |
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130 | subset_(new SubsetGenerator<D>(sampler,data,fs)) |
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131 | { |
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132 | } |
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133 | |
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134 | |
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135 | template <class C, class D> |
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136 | EnsembleBuilder<C, D>::~EnsembleBuilder(void) |
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137 | { |
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138 | for(size_t i=0; i<classifier_.size(); i++) |
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139 | delete classifier_[i]; |
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140 | delete subset_; |
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141 | } |
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142 | |
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143 | |
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144 | template <class C, class D> |
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145 | void EnsembleBuilder<C, D>::build(void) |
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146 | { |
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147 | for(u_long i=0; i<subset_->size();++i) { |
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148 | C* classifier = mother_.make_classifier(subset_->training_data(i), |
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149 | subset_->training_target(i)); |
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150 | classifier->train(); |
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151 | classifier_.push_back(classifier); |
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152 | } |
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153 | } |
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154 | |
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155 | |
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156 | template <class C, class D> |
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157 | const C& EnsembleBuilder<C, D>::classifier(size_t i) const |
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158 | { |
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159 | return *(classifier_[i]); |
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160 | } |
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161 | |
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162 | |
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163 | template <class C, class D> |
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164 | u_long EnsembleBuilder<C, D>::size(void) const |
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165 | { |
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166 | return classifier_.size(); |
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167 | } |
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168 | |
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169 | |
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170 | template <class C, class D> |
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171 | void EnsembleBuilder<C, D>::predict |
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172 | (const D& data, std::vector<std::vector<statistics::Averager> >& result) |
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173 | { |
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174 | result.clear(); |
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175 | result.reserve(subset_->target().nof_classes()); |
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176 | for(size_t i=0; i<subset_->target().nof_classes();i++) |
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177 | result.push_back(std::vector<statistics::Averager>(data.columns())); |
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178 | |
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179 | utility::matrix prediction; |
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180 | |
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181 | for(u_long k=0;k<subset_->size();++k) { |
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182 | const D* sub_data = |
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183 | data.selected(subset_->training_features(k)); |
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184 | assert(sub_data); |
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185 | classifier(k).predict(*sub_data,prediction); |
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186 | delete sub_data; |
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187 | } |
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188 | |
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189 | for(size_t i=0; i<prediction.rows();i++) |
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190 | for(size_t j=0; j<prediction.columns();j++) |
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191 | result[i][j].add(prediction(i,j)); |
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192 | } |
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193 | |
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194 | |
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195 | template <class C, class D> |
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196 | const std::vector<std::vector<statistics::Averager> >& |
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197 | EnsembleBuilder<C, D>::validate(void) |
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198 | { |
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199 | validation_result_.clear(); |
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200 | |
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201 | validation_result_.reserve(subset_->target().nof_classes()); |
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202 | for(size_t i=0; i<subset_->target().nof_classes();i++) |
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203 | validation_result_.push_back(std::vector<statistics::Averager>(subset_->target().size())); |
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204 | |
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205 | utility::matrix prediction; |
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206 | for(u_long k=0;k<subset_->size();k++) { |
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207 | classifier(k).predict(subset_->validation_data(k),prediction); |
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208 | |
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209 | // map results to indices of samples in training + validation data set |
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210 | for(size_t i=0; i<prediction.rows();i++) |
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211 | for(size_t j=0; j<prediction.columns();j++) { |
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212 | validation_result_[i][subset_->validation_index(k)[j]]. |
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213 | add(prediction(i,j)); |
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214 | } |
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215 | } |
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216 | return validation_result_; |
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217 | } |
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218 | |
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219 | }}} // of namespace classifier, yat, and theplu |
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220 | |
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221 | #endif |
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