mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
96 lines
3.4 KiB
96 lines
3.4 KiB
#include "opencv2/ml/ml.hpp" |
|
#include <stdio.h> |
|
|
|
void help() |
|
{ |
|
printf( |
|
"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees:\n" |
|
"CvDTree dtree;\n" |
|
"CvBoost boost;\n" |
|
"CvRTrees rtrees;\n" |
|
"CvERTrees ertrees;\n" |
|
"CvGBTrees gbtrees;\n" |
|
"Date is hard coded to come from filename = \"../../../opencv/samples/c/waveform.data\";\n" |
|
"Or can come from filename = \"../../../opencv/samples/c/waveform.data\";\n" |
|
"Call:\n" |
|
"./tree_engine\n\n"); |
|
} |
|
void print_result(float train_err, float test_err, const CvMat* var_imp) |
|
{ |
|
printf( "train error %f\n", train_err ); |
|
printf( "test error %f\n\n", test_err ); |
|
|
|
if (var_imp) |
|
{ |
|
bool is_flt = false; |
|
if ( CV_MAT_TYPE( var_imp->type ) == CV_32FC1) |
|
is_flt = true; |
|
printf( "variable impotance\n" ); |
|
for( int i = 0; i < var_imp->cols; i++) |
|
{ |
|
printf( "%d %f\n", i, is_flt ? var_imp->data.fl[i] : var_imp->data.db[i] ); |
|
} |
|
} |
|
printf("\n"); |
|
} |
|
|
|
int main() |
|
{ |
|
const int train_sample_count = 300; |
|
|
|
#define LEPIOTA //Turn on discrete data set |
|
#ifdef LEPIOTA //Of course, you might have to set the path here to what's on your machine ... |
|
const char* filename = "../../opencv/samples/c/agaricus-lepiota.data"; |
|
#else |
|
const char* filename = "../../opencv/samples/c/waveform.data"; |
|
#endif |
|
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); |
|
CvDTree dtree; |
|
CvBoost boost; |
|
CvRTrees rtrees; |
|
CvERTrees ertrees; |
|
CvGBTrees gbtrees; |
|
|
|
CvMLData data; |
|
|
|
CvTrainTestSplit spl( train_sample_count ); |
|
|
|
if ( data.read_csv( filename ) == 0) |
|
{ |
|
|
|
#ifdef LEPIOTA |
|
data.set_response_idx( 0 ); |
|
#else |
|
data.set_response_idx( 21 ); |
|
data.change_var_type( 21, CV_VAR_CATEGORICAL ); |
|
#endif |
|
|
|
data.set_train_test_split( &spl ); |
|
|
|
printf("======DTREE=====\n"); |
|
dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 )); |
|
print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() ); |
|
|
|
#ifdef LEPIOTA |
|
printf("======BOOST=====\n"); |
|
boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0)); |
|
print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance |
|
#endif |
|
|
|
printf("======RTREES=====\n"); |
|
rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER )); |
|
print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() ); |
|
|
|
printf("======ERTREES=====\n"); |
|
ertrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER )); |
|
print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() ); |
|
|
|
printf("======GBTREES=====\n"); |
|
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.05f, 0.6f, 10, true)); |
|
print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance |
|
} |
|
else |
|
printf("File can not be read"); |
|
|
|
return 0; |
|
}
|
|
|