CvRTParams::CvRTParams(int max_depth, int min_sample_count, float regression_accuracy, bool use_surrogates, int max_categories, const float* priors, bool calc_var_importance, int nactive_vars, int max_num_of_trees_in_the_forest, float forest_accuracy, int termcrit_type)

#include <cv.h>       // opencv general include file
#include <ml.h>          // opencv machine learning include file
#include <stdio.h>using namespace cv; // OpenCV API is in the C++ "cv" namespace/******************************************************************************/
// global definitions (for speed and ease of use)
//手写体数字识别#define NUMBER_OF_TRAINING_SAMPLES 60000
#define ATTRIBUTES_PER_SAMPLE 784
#define NUMBER_OF_TESTING_SAMPLES 10000#define NUMBER_OF_CLASSES 10// N.B. classes are integer handwritten digits in range 0-9/******************************************************************************/// loads the sample database from file (which is a CSV text file)

inline void revertInt(int&x)
{x=((x&0x000000ff)<<24)|((x&0x0000ff00)<<8)|((x&0x00ff0000)>>8)|((x&0xff000000)>>24);
};int read_data_from_csv(const char* samplePath,const char* labelPath, Mat data, Mat classes,int n_samples )
{FILE* sampleFile=fopen(samplePath,"rb");FILE* labelFile=fopen(labelPath,"rb");int mbs=0,number=0,col=0,row=0;fread(&mbs,4,1,sampleFile);fread(&number,4,1,sampleFile);fread(&row,4,1,sampleFile);fread(&col,4,1,sampleFile);revertInt(mbs);revertInt(number);revertInt(row);revertInt(col);fread(&mbs,4,1,labelFile);fread(&number,4,1,labelFile);revertInt(mbs);revertInt(number);unsigned char temp;for(int line = 0; line < n_samples; line++){// for each attribute on the line in the filefor(int attribute = 0; attribute < (ATTRIBUTES_PER_SAMPLE + 1); attribute++){if (attribute < ATTRIBUTES_PER_SAMPLE){// first 64 elements (0-63) in each line are the attributesfread(&temp,1,1,sampleFile);//fscanf(f, "%f,", &tmp);data.at<float>(line, attribute) = static_cast<float>(temp);// printf("%f,", data.at<float>(line, attribute));
            }else if (attribute == ATTRIBUTES_PER_SAMPLE){// attribute 65 is the class label {0 ... 9}fread(&temp,1,1,labelFile);//fscanf(f, "%f,", &tmp);classes.at<float>(line, 0) = static_cast<float>(temp);// printf("%f\n", classes.at<float>(line, 0));
            }}}fclose(sampleFile);fclose(labelFile);return 1; // all OK
}/******************************************************************************/int main( int argc, char** argv )
{for (int i=0; i< argc; i++)std::cout<<argv[i]<<std::endl;// lets just check the version firstprintf ("OpenCV version %s (%d.%d.%d)\n",CV_VERSION,CV_MAJOR_VERSION, CV_MINOR_VERSION, CV_SUBMINOR_VERSION);//定义训练数据与标签矩阵Mat training_data = Mat(NUMBER_OF_TRAINING_SAMPLES, ATTRIBUTES_PER_SAMPLE, CV_32FC1);Mat training_classifications = Mat(NUMBER_OF_TRAINING_SAMPLES, 1, CV_32FC1);//定义测试数据矩阵与标签Mat testing_data = Mat(NUMBER_OF_TESTING_SAMPLES, ATTRIBUTES_PER_SAMPLE, CV_32FC1);Mat testing_classifications = Mat(NUMBER_OF_TESTING_SAMPLES, 1, CV_32FC1);// define all the attributes as numerical// alternatives are CV_VAR_CATEGORICAL or CV_VAR_ORDERED(=CV_VAR_NUMERICAL)// that can be assigned on a per attribute basis
Mat var_type = Mat(ATTRIBUTES_PER_SAMPLE + 1, 1, CV_8U );var_type.setTo(Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical// this is a classification problem (i.e. predict a discrete number of class// outputs) so reset the last (+1) output var_type element to CV_VAR_CATEGORICAL
var_type.at<uchar>(ATTRIBUTES_PER_SAMPLE, 0) = CV_VAR_CATEGORICAL;double result; // value returned from a prediction//加载训练数据集和测试数据集if (read_data_from_csv(argv[1],argv[2], training_data, training_classifications, NUMBER_OF_TRAINING_SAMPLES) &&read_data_from_csv(argv[3],argv[4], testing_data, testing_classifications, NUMBER_OF_TESTING_SAMPLES)){/********************************步骤1：定义初始化Random Trees的参数******************************/float priors[] = {1,1,1,1,1,1,1,1,1,1};  // weights of each classification for classesCvRTParams params = CvRTParams(20, // max depth50, // min sample count0, // regression accuracy: N/A herefalse, // compute surrogate split, no missing data15, // max number of categories (use sub-optimal algorithm for larger numbers)priors, // the array of priorsfalse,  // calculate variable importance50,       // number of variables randomly selected at node and used to find the best split(s).100,     // max number of trees in the forest0.01f,                // forest accuracyCV_TERMCRIT_ITER |    CV_TERMCRIT_EPS // termination cirteria
                                      );/****************************步骤2：训练 Random Decision Forest(RDF)分类器*********************/printf( "\nUsing training database: %s\n\n", argv[1]);CvRTrees* rtree = new CvRTrees;bool train_result=rtree->train(training_data, CV_ROW_SAMPLE, training_classifications,Mat(), Mat(), var_type, Mat(), params);
//        float train_error=rtree->get_train_error();
//        printf("train error:%f\n",train_error);// perform classifier testing and report results
        Mat test_sample;int correct_class = 0;int wrong_class = 0;int false_positives [NUMBER_OF_CLASSES] = {0,0,0,0,0,0,0,0,0,0};printf( "\nUsing testing database: %s\n\n", argv[2]);for (int tsample = 0; tsample < NUMBER_OF_TESTING_SAMPLES; tsample++){// extract a row from the testing matrixtest_sample = testing_data.row(tsample);/********************************步骤3：预测*********************************************/result = rtree->predict(test_sample, Mat());printf("Testing Sample %i -> class result (digit %d)\n", tsample, (int) result);// if the prediction and the (true) testing classification are the same// (N.B. openCV uses a floating point decision tree implementation!)if (fabs(result - testing_classifications.at<float>(tsample, 0))>= FLT_EPSILON){// if they differ more than floating point error => wrong classwrong_class++;false_positives[(int) result]++;}else{// otherwise correctcorrect_class++;}}printf( "\nResults on the testing database: %s\n""\tCorrect classification: %d (%g%%)\n""\tWrong classifications: %d (%g%%)\n",argv[2],correct_class, (double) correct_class*100/NUMBER_OF_TESTING_SAMPLES,wrong_class, (double) wrong_class*100/NUMBER_OF_TESTING_SAMPLES);for (int i = 0; i < NUMBER_OF_CLASSES; i++){printf( "\tClass (digit %d) false postives     %d (%g%%)\n", i,false_positives[i],(double) false_positives[i]*100/NUMBER_OF_TESTING_SAMPLES);}// all matrix memory free by destructors// all OK : main returns 0return 0;}// not OK : main returns -1return -1;
}