generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Set stricter warning rules for gcc

Browse Source
This commit is contained in:
Andrey Kamaev
2012-06-07 17:21:29 +00:00
parent 0395f7c63f
commit 49a1ba6038
241 changed files with 9054 additions and 8947 deletions
Download Patch File Download Diff File Expand all files Collapse all files

50
samples/c/tree_engine.cpp
View File

@@ -3,23 +3,23 @@
#include <stdio.h>
#include <map>
void help()
static 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"
"Call:\n\t./tree_engine [-r <response_column>] [-c] <csv filename>\n"
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"
"Call:\n\t./tree_engine [-r <response_column>] [-c] <csv filename>\n"
"where -r <response_column> specified the 0-based index of the response (0 by default)\n"
"-c specifies that the response is categorical (it's ordered by default) and\n"
"<csv filename> is the name of training data file in comma-separated value format\n\n");
}
int count_classes(CvMLData& data)
static int count_classes(CvMLData& data)
{
cv::Mat r(data.get_responses());
std::map<int, int> rmap;
@@ -30,26 +30,26 @@ int count_classes(CvMLData& data)
int ival = cvRound(val);
if( ival != val )
return -1;
rmap[ival] = 1;
rmap[ival] = 1;
}
return (int)rmap.size();
}
void print_result(float train_err, float test_err, const CvMat* _var_imp)
static 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)
{
cv::Mat var_imp(_var_imp), sorted_idx;
cv::sortIdx(var_imp, sorted_idx, CV_SORT_EVERY_ROW + CV_SORT_DESCENDING);
printf( "variable importance:\n" );
int i, n = (int)var_imp.total();
int type = var_imp.type();
CV_Assert(type == CV_32F || type == CV_64F);
for( i = 0; i < n; i++)
{
int k = sorted_idx.at<int>(i);
@@ -69,7 +69,7 @@ int main(int argc, char** argv)
const char* filename = 0;
int response_idx = 0;
bool categorical_response = false;
for(int i = 1; i < argc; i++)
{
if(strcmp(argv[i], "-r") == 0)
@@ -85,26 +85,26 @@ int main(int argc, char** argv)
return -1;
}
}
printf("\nReading in %s...\n\n",filename);
CvDTree dtree;
CvBoost boost;
CvRTrees rtrees;
CvERTrees ertrees;
CvGBTrees gbtrees;
CvGBTrees gbtrees;
CvMLData data;
CvTrainTestSplit spl( 0.5f );
if ( data.read_csv( filename ) == 0)
{
data.set_response_idx( response_idx );
if(categorical_response)
data.change_var_type( response_idx, CV_VAR_CATEGORICAL );
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() );
@@ -125,10 +125,10 @@ int main(int argc, char** argv)
print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
printf("======GBTREES=====\n");
if (categorical_response)
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.1f, 0.8f, 5, false));
else
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::SQUARED_LOSS, 100, 0.1f, 0.8f, 5, false));
if (categorical_response)
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.1f, 0.8f, 5, false));
else
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::SQUARED_LOSS, 100, 0.1f, 0.8f, 5, false));
print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
}
else