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made everything compile and even run somehow

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This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
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128
samples/cpp/tree_engine.cpp
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@@ -1,63 +1,35 @@
#include "opencv2/ml/ml.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#include "opencv2/core/utility.hpp"
#include <stdio.h>
#include <string>
#include <map>
using namespace cv;
using namespace cv::ml;
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"
"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees.\n"
"Usage:\n\t./tree_engine [-r <response_column>] [-ts type_spec] <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"
"-ts specifies the var type spec in the form ord[n1,n2-n3,n4-n5,...]cat[m1-m2,m3,m4-m5,...]\n"
"<csv filename> is the name of training data file in comma-separated value format\n\n");
}
static int count_classes(CvMLData& data)
static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data)
{
cv::Mat r = cv::cvarrToMat(data.get_responses());
std::map<int, int> rmap;
int i, n = (int)r.total();
for( i = 0; i < n; i++ )
bool ok = model->train(data);
if( !ok )
{
float val = r.at<float>(i);
int ival = cvRound(val);
if( ival != val )
return -1;
rmap[ival] = 1;
printf("Training failed\n");
}
return (int)rmap.size();
}
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)
else
{
cv::Mat var_imp = cv::cvarrToMat(_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);
printf( "%d\t%f\n", k, type == CV_32F ? var_imp.at<float>(k) : var_imp.at<double>(k));
}
printf( "train error: %f\n", model->calcError(data, false, noArray()) );
printf( "test error: %f\n\n", model->calcError(data, true, noArray()) );
}
printf("\n");
}
int main(int argc, char** argv)
@@ -69,14 +41,14 @@ int main(int argc, char** argv)
}
const char* filename = 0;
int response_idx = 0;
bool categorical_response = false;
std::string typespec;
for(int i = 1; i < argc; i++)
{
if(strcmp(argv[i], "-r") == 0)
sscanf(argv[++i], "%d", &response_idx);
else if(strcmp(argv[i], "-c") == 0)
categorical_response = true;
else if(strcmp(argv[i], "-ts") == 0)
typespec = argv[++i];
else if(argv[i][0] != '-' )
filename = argv[i];
else
@@ -88,52 +60,32 @@ int main(int argc, char** argv)
}
printf("\nReading in %s...\n\n",filename);
CvDTree dtree;
CvBoost boost;
CvRTrees rtrees;
CvERTrees ertrees;
CvGBTrees gbtrees;
const double train_test_split_ratio = 0.5;
CvMLData data;
Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx+1, typespec);
CvTrainTestSplit spl( 0.5f );
if ( data.read_csv( filename ) == 0)
if( data.empty() )
{
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() );
if( categorical_response && count_classes(data) == 2 )
{
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
}
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( 18, 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");
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
printf("ERROR: File %s can not be read\n", filename);
return 0;
}
else
printf("File can not be read");
data->setTrainTestSplitRatio(train_test_split_ratio);
printf("======DTREE=====\n");
Ptr<DTrees> dtree = DTrees::create(DTrees::Params( 10, 2, 0, false, 16, 0, false, false, Mat() ));
train_and_print_errs(dtree, data);
if( (int)data->getClassLabels().total() <= 2 ) // regression or 2-class classification problem
{
printf("======BOOST=====\n");
Ptr<Boost> boost = Boost::create(Boost::Params(Boost::GENTLE, 100, 0.95, 2, false, Mat()));
train_and_print_errs(boost, data);
}
printf("======RTREES=====\n");
Ptr<RTrees> rtrees = RTrees::create(RTrees::Params(10, 2, 0, false, 16, Mat(), false, 0, TermCriteria(TermCriteria::MAX_ITER, 100, 0)));
train_and_print_errs(rtrees, data);
return 0;
}