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Open Source Computer Vision Library
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84 lines
2.7 KiB
84 lines
2.7 KiB
#include "opencv2/ml/ml.hpp" |
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#include <stdio.h> |
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/* |
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The sample demonstrates how to use different decision trees. |
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*/ |
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void print_result(float train_err, float test_err, const CvMat* var_imp) |
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{ |
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printf( "train error %f\n", train_err ); |
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printf( "test error %f\n\n", test_err ); |
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if (var_imp) |
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{ |
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bool is_flt = false; |
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if ( CV_MAT_TYPE( var_imp->type ) == CV_32FC1) |
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is_flt = true; |
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printf( "variable impotance\n" ); |
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for( int i = 0; i < var_imp->cols; i++) |
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{ |
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printf( "%d %f\n", i, is_flt ? var_imp->data.fl[i] : var_imp->data.db[i] ); |
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} |
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} |
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printf("\n"); |
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} |
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int main() |
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{ |
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const int train_sample_count = 300; |
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//#define LEPIOTA |
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#ifdef LEPIOTA |
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const char* filename = "../../../OpenCV/samples/c/agaricus-lepiota.data"; |
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#else |
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const char* filename = "../../../OpenCV/samples/c/waveform.data"; |
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#endif |
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CvDTree dtree; |
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CvBoost boost; |
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CvRTrees rtrees; |
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CvERTrees ertrees; |
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CvGBTrees gbtrees; |
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CvMLData data; |
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CvTrainTestSplit spl( train_sample_count ); |
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if ( data.read_csv( filename ) == 0) |
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{ |
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#ifdef LEPIOTA |
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data.set_response_idx( 0 ); |
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#else |
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data.set_response_idx( 21 ); |
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data.change_var_type( 21, CV_VAR_CATEGORICAL ); |
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#endif |
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data.set_train_test_split( &spl ); |
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printf("======DTREE=====\n"); |
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dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 )); |
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print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() ); |
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#ifdef LEPIOTA |
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printf("======BOOST=====\n"); |
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boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0)); |
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print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data ), 0 ); |
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#endif |
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printf("======RTREES=====\n"); |
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rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER )); |
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print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() ); |
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printf("======ERTREES=====\n"); |
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ertrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER )); |
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print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() ); |
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printf("======GBTREES=====\n"); |
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gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.05f, 0.6f, 10, true)); |
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print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); |
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} |
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else |
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printf("File can not be read"); |
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return 0; |
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}
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