随机决策森林——OpenCV类CvRTrees使用实例

本文介绍：OpenCV机器学习库MLL中随机森林Random Trees的使用

参考文献：

1.Breiman, Leo (2001). "Random Forests". Machine Learning 

2.Random Forests网站

不熟悉MLL的参考此文：OpenCV机器学习库MLL

OpenCV的机器学习算法都比较简单：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* 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;
};

使用CvRTrees类，来对手写体数据作分类

// Example : random forest (tree) learning
// Author : Toby Breckon, [email protected]

// Copyright (c) 2011 School of Engineering, Cranfield University
// License : LGPL - http://www.gnu.org/licenses/lgpl.html

#include        // opencv general include file
#include 		  // opencv machine learning include file
#include 

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 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(line, attribute) = tmp;
                // printf("%f,", data.at(line, attribute));
            }
            else if (attribute == 64)
            {
                // attribute 65 is the class label {0 ... 9}
                fscanf(f, "%f,", &tmp);
                classes.at(line, 0) = tmp;
                // printf("%f\n", classes.at(line, 0));
            }
        }
    }

    fclose(f);
    return 1; // all OK
}

/******************************************************************************/

int main( int argc, char** argv )
{
    
	for (int i=0; i< argc; i++)
		std::cout<(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], training_data, training_classifications, NUMBER_OF_TRAINING_SAMPLES) &&
            read_data_from_csv(argv[2], 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 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_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;
        rtree->train(training_data, CV_ROW_SAMPLE, training_classifications,
                     Mat(), Mat(), var_type, Mat(), params);

        // 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(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;
}
/******************************************************************************/

=============================================================================

手写体数据：

设置数据集 train test：

在test数据集上的正确率：