OpenCV码源笔记——tree_engine（CvDTree,CvBoost,CvRTrees,CvERTrees,CvGBTrees）

CvDTree,CvBoost,CvRTrees,CvERTrees,CvGBTrees都是读取.csv格式样本：dtree.train( &data, CvDTreeParams())

dtree.calc_error返回错误率（对于分类来说返回分类样本的错误率，对于回归情况来说是样本的平方误差）

dtree.get_var_importance()：Returns the variable importance array.

#include "opencv2/ml/ml.hpp"
#include "opencv2/core/core_c.h"
#include <stdio.h>
#include <map>
//都是读取.csv格式样本：dtree.train( &data, CvDTreeParams())
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"
		"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)
{
	cv::Mat r(data.get_responses());
	std::map<int, int> rmap;
	int i, n = (int)r.total();
	for( i = 0; i < n; i++ )
	{
		float val = r.at<float>(i);
		int ival = cvRound(val);
		if( ival != val )
			return -1;
		rmap[ival] = 1; 
	}
	return rmap.size();
}

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);
			printf( "%d\t%f\n", k, type == CV_32F ? var_imp.at<float>(k) : var_imp.at<double>(k));
		}
	}
	printf("\n");
}

int main(int argc, char** argv)
{
	if(argc < 2)
	{
		help();
		return 0;
	}
	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)
			sscanf(argv[++i], "%d", &response_idx);
		else if(strcmp(argv[i], "-c") == 0)
			categorical_response = true;
		else if(argv[i][0] != '-' )
			filename = argv[i];
		else
		{
			printf("Error. Invalid option %s\n", argv[i]);
			help();
			return -1;
		}
	}

	printf("\nReading in %s...\n\n",filename);
	CvDTree dtree;
	CvBoost boost;
	CvRTrees rtrees;
	CvERTrees ertrees;
	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 ));//CvMLData形式load .csv文件;
		print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() );
		//dtree.calc_error返回错误率（对于分类来说返回分类样本的错误率，对于回归情况来说是样本的平方误差。dtree.get_var_importance()：Returns the variable importance array.）
		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( 10, 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
	}
	else
		printf("File can not be read");

	return 0;
}