程序片段----隨機森林 opencv

	cv::Mat trainData;
	cv::Mat ClassData;
	// ... push_back trainData and ClassData, list features as row sample; set type 0-9 for example as a class value

	// train
	cv::Ptr<cv::ml::RTrees> rtrees = cv::ml::RTrees::create();
	rtrees->setMaxDepth(4);
	rtrees->setMinSampleCount(2);
	rtrees->setRegressionAccuracy(0.f);
	rtrees->setUseSurrogates(false);
	rtrees->setMaxCategories(16);
	rtrees->setPriors(cv::Mat());
	rtrees->setCalculateVarImportance(false);
	rtrees->setActiveVarCount(1);
	rtrees->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 5, 0));
	cv::Ptr<cv::ml::TrainData> tData = cv::ml::TrainData::create(trainData, cv::ml::ROW_SAMPLE, ClassData);
	rtrees->train(tData);
	rtrees->save("trainingFile.xml");

	// test
	cv::Ptr<cv::ml::RTrees> prtrees2 = cv::ml::RTrees::load<cv::ml::RTrees>("trainingFile.xml");
	cv::Mat testMat;
	// ... row sample

	float response = prtrees2->predict(testMat);
	// ... compare the testMat with Class, to know the correct rate