f4334277
 Hu Chunming
提交3rdparty
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#include "opencv2/ml.hpp"
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include <stdio.h>
#include <string>
#include <map>
using namespace cv;
using namespace cv::ml;
static void help(char** argv)
{
printf(
"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees.\n"
"Usage:\n\t%s [-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"
"-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", argv[0]);
}
static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data)
{
bool ok = model->train(data);
if( !ok )
{
printf("Training failed\n");
}
else
{
printf( "train error: %f\n", model->calcError(data, false, noArray()) );
printf( "test error: %f\n\n", model->calcError(data, true, noArray()) );
}
}
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, "{ help h | | }{r | 0 | }{ts | | }{@input | | }");
if (parser.has("help"))
{
help(argv);
return 0;
}
std::string filename = parser.get<std::string>("@input");
int response_idx;
std::string typespec;
response_idx = parser.get<int>("r");
typespec = parser.get<std::string>("ts");
if( filename.empty() || !parser.check() )
{
parser.printErrors();
help(argv);
return 0;
}
printf("\nReading in %s...\n\n",filename.c_str());
const double train_test_split_ratio = 0.5;
Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx+1, typespec);
if( data.empty() )
{
printf("ERROR: File %s can not be read\n", filename.c_str());
return 0;
}
data->setTrainTestSplitRatio(train_test_split_ratio);
std::cout << "Test/Train: " << data->getNTestSamples() << "/" << data->getNTrainSamples();
printf("======DTREE=====\n");
Ptr<DTrees> dtree = DTrees::create();
dtree->setMaxDepth(10);
dtree->setMinSampleCount(2);
dtree->setRegressionAccuracy(0);
dtree->setUseSurrogates(false);
dtree->setMaxCategories(16);
dtree->setCVFolds(0);
dtree->setUse1SERule(false);
dtree->setTruncatePrunedTree(false);
dtree->setPriors(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->setBoostType(Boost::GENTLE);
boost->setWeakCount(100);
boost->setWeightTrimRate(0.95);
boost->setMaxDepth(2);
boost->setUseSurrogates(false);
boost->setPriors(Mat());
train_and_print_errs(boost, data);
}
printf("======RTREES=====\n");
Ptr<RTrees> rtrees = RTrees::create();
rtrees->setMaxDepth(10);
rtrees->setMinSampleCount(2);
rtrees->setRegressionAccuracy(0);
rtrees->setUseSurrogates(false);
rtrees->setMaxCategories(16);
rtrees->setPriors(Mat());
rtrees->setCalculateVarImportance(true);
rtrees->setActiveVarCount(0);
rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 0));
train_and_print_errs(rtrees, data);
cv::Mat ref_labels = data->getClassLabels();
cv::Mat test_data = data->getTestSampleIdx();
cv::Mat predict_labels;
rtrees->predict(data->getSamples(), predict_labels);
cv::Mat variable_importance = rtrees->getVarImportance();
std::cout << "Estimated variable importance" << std::endl;
for (int i = 0; i < variable_importance.rows; i++) {
std::cout << "Variable " << i << ": " << variable_importance.at<float>(i, 0) << std::endl;
}
return 0;
}
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