void BaseTest::run( int start_from )
{
int test_case_idx, count = get_test_case_count();
int64 t_start = cvGetTickCount();
double freq = cv::getTickFrequency();
bool ff = can_do_fast_forward();
int progress = 0, code;
int64 t1 = t_start;
for( test_case_idx = ff && start_from >= 0 ? start_from : 0;
count < 0 || test_case_idx < count; test_case_idx++ )
{
ts->update_context( this, test_case_idx, ff );
progress = update_progress( progress, test_case_idx, count, (double)(t1 - t_start)/(freq*1000) );
code = prepare_test_case( test_case_idx );
if( code < 0 || ts->get_err_code() < 0 )
return;
if( code == 0 )
continue;
run_func();
if( ts->get_err_code() < 0 )
return;
if( validate_test_results( test_case_idx ) < 0 || ts->get_err_code() < 0 )
return;
}
}
int main() {
adj_table adj = prepare_test_case();
adj_table::TopologicalOrderContainer order;
adj.topological_sort(order);
const size_t from = 0, to = 1;
printf("%u path(s) from %u to %u.\n", count_paths(adj, order, from, to), from, to);
}
int CV_SLMLTest::run_test_case( int testCaseIdx )
{
int code = CvTS::OK;
code = prepare_test_case( testCaseIdx );
if( code == CvTS::OK )
{
data.mix_train_and_test_idx();
code = train( testCaseIdx );
if( code == CvTS::OK )
{
get_error( testCaseIdx, CV_TEST_ERROR, &test_resps1 );
save( tmpnam( fname1 ) );
load( fname1);
get_error( testCaseIdx, CV_TEST_ERROR, &test_resps2 );
save( tmpnam( fname2 ) );
}
else
ts->printf( CvTS::LOG, "model can not be trained" );
}
return code;
}
int main() {
adj_table adj = prepare_test_case();
dfs(adj);
}
