97 lines
3.4 KiB
C++
97 lines
3.4 KiB
C++
#include "opencv2/ml/ml.hpp"
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#include <stdio.h>
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void help()
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{
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printf(
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"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees:\n"
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"CvDTree dtree;\n"
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"CvBoost boost;\n"
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"CvRTrees rtrees;\n"
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"CvERTrees ertrees;\n"
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"CvGBTrees gbtrees;\n"
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"Date is hard coded to come from filename = \"../../../opencv/samples/c/waveform.data\";\n"
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"Or can come from filename = \"../../../opencv/samples/c/waveform.data\";\n"
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"Call:\n"
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"./tree_engine\n\n");
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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 //Turn on discrete data set
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#ifdef LEPIOTA //Of course, you might have to set the path here to what's on your machine ...
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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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printf("\n Reading in %s. If it is not found, you may have to change this hard-coded path in tree_engine.cpp\n\n",filename);
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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, CV_TEST_ERROR ), 0 ); //doesn't compute importance
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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 ); //doesn't compute importance
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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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