隨機森林演算法原理詳解與實現步驟

阿新 • • 發佈：2019-01-04

#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)

//手寫體數字識別 #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 file for(int attribute = 0; attribute < (ATTRIBUTES_PER_SAMPLE + 1); attribute++) { if (attribute < ATTRIBUTES_PER_SAMPLE) { // first 64 elements (0-63) in each line are the attributes fread(&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 first printf ("OpenCV version %s (%d.%d.%d)\n", CV_VERSION, CV_MAJOR_VERSION, CV_MINOR_VERSION, CV_SUBMINOR_VERSION); //定義訓練資料與標籤矩陣 Mat training_data = Mat(NUMBER_OF_TRAINING_SAMPLES, ATTRIBUTES_PER_SAMPLE, CV_32FC1); Mat training_classifications = Mat(NUMBER_OF_TRAINING_SAMPLES, 1, CV_32FC1); //定義測試資料矩陣與標籤 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 //載入訓練資料集和測試資料集 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)) { /********************************步驟1：定義初始化Random Trees的引數******************************/ float priors[] = {1,1,1,1,1,1,1,1,1,1}; // weights of each classification for classes CvRTParams params = CvRTParams(20, // max depth 50, // min sample count 0, // regression accuracy: N/A here false, // compute surrogate split, no missing data 15, // max number of categories (use sub-optimal algorithm for larger numbers) priors, // the array of priors false, // calculate variable importance 50, // number of variables randomly selected at node and used to find the best split(s). 100, // max number of trees in the forest 0.01f, // forest accuracy CV_TERMCRIT_ITER | CV_TERMCRIT_EPS // termination cirteria ); /****************************步驟2：訓練 Random Decision Forest(RDF)分類器*********************/ 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 matrix test_sample = testing_data.row(tsample); /********************************步驟3：預測*********************************************/ 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 class wrong_class++; false_positives[(int) result]++; } else { // otherwise correct correct_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 0 return 0; } // not OK : main returns -1 return -1; }

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