Random decision forest-Examples of cvrtrees for opencv

This article introduces the use of random forest random trees in the MLL of opencv Machine Learning Library.

References:

1. breiman,
LEO (2001). "random forests ".Machine
Learning 

2. Random Forests website

If you are not familiar with MLL, refer to this article: opencv Machine Learning Library MLL

Opencv machine learning algorithms are relatively simple: train --> predict

class CV_EXPORTS_W CvRTrees : public CvStatModel{public:    CV_WRAP CvRTrees();    virtual ~CvRTrees();    virtual bool train( const CvMat* trainData, int tflag,                        const CvMat* responses, const CvMat* varIdx=0,                        const CvMat* sampleIdx=0, const CvMat* varType=0,                        const CvMat* missingDataMask=0,                        CvRTParams params=CvRTParams() );    virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );    virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;    virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;    CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,                       const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),                       const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),                       const cv::Mat& missingDataMask=cv::Mat(),                       CvRTParams params=CvRTParams() );    CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;    CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;    CV_WRAP virtual cv::Mat getVarImportance();    CV_WRAP virtual void clear();    virtual const CvMat* get_var_importance();    virtual float get_proximity( const CvMat* sample1, const CvMat* sample2,        const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const;    virtual float calc_error( CvMLData* data, int type , std::vector<float>* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}    virtual float get_train_error();    virtual void read( CvFileStorage* fs, CvFileNode* node );    virtual void write( CvFileStorage* fs, const char* name ) const;    CvMat* get_active_var_mask();    CvRNG* get_rng();    int get_tree_count() const;    CvForestTree* get_tree(int i) const;protected:    virtual std::string getName() const;    virtual bool grow_forest( const CvTermCriteria term_crit );    // array of the trees of the forest    CvForestTree** trees;    CvDTreeTrainData* data;    int ntrees;    int nclasses;    double oob_error;    CvMat* var_importance;    int nsamples;    cv::RNG* rng;    CvMat* active_var_mask;};

Use the cvrtrees class to classify handwritten data

// Example: Random forest (tree) Learning // Author: Toby breckon, toby.breckon@cranfield.ac.uk // copyright (c) 2011 School of Engineering, Cranfield University // license: lgpl-http://www.gnu.org/licenses/lgpl.html#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 usage of use) // handwritten number recognition # define number_of_training_samples 3823 # define attributes_per_sample 64 # define number_of_testing_samples 1797 # 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) int read_data_from_csv (const char * filename, mat data, mat classes, int n_samples) {float TMP; // if we can't read the input file then return 0 file * f = fopen (filename, "R"); If (! F) {printf ("error: cannot read file % s \ n", filename); Return 0; // all not OK} // for each sample in the file 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 <64) {// first 64 elements (0-63) in each line are the attributes fscanf (F, "% F,", & TMP); data. at <flo At> (line, attribute) = TMP; // printf ("% F,", Data. at <float> (line, attribute);} else if (attribute = 64) {// attribute 65 is the class label {0... 9} fscanf (F, "% F,", & TMP); classes. at <float> (line, 0) = TMP; // printf ("% F \ n", classes. at <float> (line, 0) ;}} fclose (f); 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); // defines the training data and label matrix mat training_data = MAT (partition, attributes_per_sample, cv_32fc1 ); mat training_classifications = MAT (number_of_training_samples, 1, cv_32fc1); // define test data Matrix and label 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 (= optional) // that can be assigned on a per attribute basis mat var_type = MAT (attributes_per_sample + 1, 1, cv_8u); var_type.s Etto (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 // load the training dataset and test dataset if (read_data_from_csv (argv [1], training_data, Training_classifications, number_of_training_samples) & read_data_from_csv (argv [2], testing_data, testing_classifications, number_of_testing_samples )) {/********************************* Step 1: define the parameter *******************************/float priors [] = {1, 1, 1, 1, 1, 1, 1}; // weights of each classification for Classes cvrtparams Params = cvrtparams (25, // max depth 5, // 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 4, // 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, // Forrest accuracy cv_term Crit_iter | cv_termcrit_eps // termination cirteria ); /*************************** Step 2: Train random demo-forest (RDF) classifier *******************/printf ("\ nusing training database: % s \ n ", argv [1]); cvrtrees * rtree = new cvrtrees; rtree-> train (training_data, cv_row_sample, training_classifications, MAT (), MAT (), var_type, MAT (), params); // perform classifier testing and report results mat test_sam Ple; int correct_class = 0; int wrong_class = 0; int false_positives [number_of_classes] = {0, 0, 0, 0, 0, 0, 0, 0}; printf ("\ nusing testing database: % s \ 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 ); /********************************* Step 3: prediction *************************************** * *****/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 demo-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/second); 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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Handwritten data:

Set the dataset train test:

Accuracy on the test Dataset: