4 The sample demonstrates how to use different decision trees.
6 void print_result(float train_err, float test_err, const CvMat* var_imp)
8 printf( "train error %f\n", train_err );
9 printf( "test error %f\n\n", test_err );
14 if ( CV_MAT_TYPE( var_imp->type ) == CV_32FC1)
16 printf( "variable impotance\n" );
17 for( int i = 0; i < var_imp->cols; i++)
19 printf( "%d %f\n", i, is_flt ? var_imp->data.fl[i] : var_imp->data.db[i] );
27 const int train_sample_count = 300;
31 const char* filename = "../../../OpenCV/samples/c/agaricus-lepiota.data";
33 const char* filename = "../../../OpenCV/samples/c/waveform.data";
43 CvTrainTestSplit spl( train_sample_count );
45 if ( data.read_csv( filename ) == 0)
49 data.set_response_idx( 0 );
51 data.set_response_idx( 21 );
52 data.change_var_type( 21, CV_VAR_CATEGORICAL );
55 data.set_train_test_split( &spl );
57 printf("======DTREE=====\n");
58 dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 ));
59 print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() );
62 printf("======BOOST=====\n");
63 boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0));
64 print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data ), 0 );
67 printf("======RTREES=====\n");
68 rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
69 print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() );
71 printf("======ERTREES=====\n");
72 ertrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
73 print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
76 printf("File can not be read");