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));elsegbtrees.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}elseprintf("File can not be read");return 0;}