Gradient Boosting Trees (CvGBTrees) added to opencv mll. Test for all CvGBTrees public methods added.

modules/ml/include/opencv2/ml/ml.hpp

@@ -183,6 +183,7 @@ CV_INLINE CvParamLattice cvDefaultParamLattice( void )
 #define CV_TYPE_NAME_ML_ANN_MLP     "opencv-ml-ann-mlp"
 #define CV_TYPE_NAME_ML_CNN         "opencv-ml-cnn"
 #define CV_TYPE_NAME_ML_RTREES      "opencv-ml-random-trees"
+#define CV_TYPE_NAME_ML_GBT         "opencv-ml-gradient-boosting-trees"
 
 #define CV_TRAIN_ERROR  0
 #define CV_TEST_ERROR   1
@@ -1359,6 +1360,532 @@ protected:
 };
 
 
+/****************************************************************************************\
+*                                   Gradient Boosted Trees                               *
+\****************************************************************************************/
+
+// DataType: STRUCT CvGBTreesParams
+// Parameters of GBT (Gradient Boosted trees model), including single
+// tree settings and ensemble parameters.
+//
+// weak_count          - count of trees in the ensemble
+// loss_function_type  - loss function used for ensemble training
+// subsample_portion   - portion of whole training set used for
+//                       every single tree training.
+//                       subsample_portion value is in (0.0, 1.0].
+//                       subsample_portion == 1.0 when whole dataset is
+//                       used on each step. Count of sample used on each
+//                       step is computed as
+//                       int(total_samples_count * subsample_portion).
+// shrinkage           - regularization parameter.
+//                       Each tree prediction is multiplied on shrinkage value.
+
+
+struct CV_EXPORTS CvGBTreesParams : public CvDTreeParams
+{
+    int weak_count;
+    int loss_function_type;
+    float subsample_portion;
+    float shrinkage;
+
+    CvGBTreesParams();
+    CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage,
+        float subsample_portion, int max_depth, bool use_surrogates );
+};
+
+// DataType: CLASS CvGBTrees
+// Gradient Boosting Trees (GBT) algorithm implementation.
+// 
+// data             - training dataset
+// params           - parameters of the CvGBTrees
+// weak             - array[0..(class_count-1)] of CvSeq
+//                    for storing tree ensembles
+// orig_response    - original responses of the training set samples
+// sum_response     - predicitons of the current model on the training dataset.
+//                    this matrix is updated on every iteration.
+// sum_response_tmp - predicitons of the model on the training set on the next
+//                    step. On every iteration values of sum_responses_tmp are
+//                    computed via sum_responses values. When the current
+//                    step is complete sum_response values become equal to
+//                    sum_responses_tmp.
+// sample_idx       - indices of samples used for training the ensemble.
+//                    CvGBTrees training procedure takes a set of samples
+//                    (train_data) and a set of responses (responses).
+//                    Only pairs (train_data[i], responses[i]), where i is
+//                    in sample_idx are used for training the ensemble.
+// subsample_train  - indices of samples used for training a single decision
+//                    tree on the current step. This indices are countered
+//                    relatively to the sample_idx, so that pairs
+//                    (train_data[sample_idx[i]], responses[sample_idx[i]])
+//                    are used for training a decision tree.
+//                    Training set is randomly splited
+//                    in two parts (subsample_train and subsample_test)
+//                    on every iteration accordingly to the portion parameter.
+// subsample_test   - relative indices of samples from the training set,
+//                    which are not used for training a tree on the current
+//                    step.
+// missing          - mask of the missing values in the training set. This
+//                    matrix has the same size as train_data. 1 - missing
+//                    value, 0 - not a missing value.
+// class_labels     - output class labels map. 
+// rng              - random number generator. Used for spliting the
+//                    training set.
+// class_count      - count of output classes.
+//                    class_count == 1 in the case of regression,
+//                    and > 1 in the case of classification.
+// delta            - Huber loss function parameter.
+// base_value       - start point of the gradient descent procedure.
+//                    model prediction is
+//                    f(x) = f_0 + sum_{i=1..weak_count-1}(f_i(x)), where
+//                    f_0 is the base value.
+
+
+
+class CV_EXPORTS CvGBTrees : public CvStatModel
+{
+public:
+
+    /*
+    // DataType: ENUM
+    // Loss functions implemented in CvGBTrees.
+    //  
+    // SQUARED_LOSS
+    // problem: regression
+    // loss = (x - x')^2
+    // 
+    // ABSOLUTE_LOSS
+    // problem: regression
+    // loss = abs(x - x')
+    // 
+    // HUBER_LOSS
+    // problem: regression
+    // loss = delta*( abs(x - x') - delta/2), if abs(x - x') > delta
+    //           1/2*(x - x')^2, if abs(x - x') <= delta,
+    //           where delta is the alpha-quantile of pseudo responses from
+    //           the training set.
+    //
+    // DEVIANCE_LOSS
+    // problem: classification
+    // 
+    */ 
+    enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
+ 
+    
+    /*
+    // Default constructor. Creates a model only (without training).
+    // Should be followed by one form of the train(...) function.
+    //
+    // API
+    // CvGBTrees();
+    
+    // INPUT
+    // OUTPUT
+    // RESULT
+    */
+    CvGBTrees();
+
+
+    /*
+    // Full form constructor. Creates a gradient boosting model and does the
+    // train.
+    //
+    // API
+    // CvGBTrees( const CvMat* _train_data, int _tflag,
+             const CvMat* _responses, const CvMat* _var_idx=0,
+             const CvMat* _sample_idx=0, const CvMat* _var_type=0,
+             const CvMat* _missing_mask=0,
+             CvGBTreesParams params=CvGBTreesParams() );
+    
+    // INPUT
+    // _train_data    - a set of input feature vectors.
+    //                  size of matrix is
+    //                  <count of samples> x <variables count>
+    //                  or <variables count> x <count of samples>
+    //                  depending on the _tflag parameter.
+    //                  matrix values are float.
+    // _tflag         - a flag showing how do samples stored in the
+    //                  _train_data matrix row by row (tflag=CV_ROW_SAMPLE)
+    //                  or column by column (tflag=CV_COL_SAMPLE).
+    // _responses     - a vector of responses corresponding to the samples
+    //                  in _train_data.
+    // _var_idx       - indices of used variables. zero value means that all
+    //                  variables are active.
+    // _sample_idx    - indices of used samples. zero value means that all
+    //                  samples from _train_data are in the training set.
+    // _var_type      - vector of <variables count> length. gives every
+    //                  variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
+    //                  _var_type = 0 means all variables are numerical.
+    // _missing_mask  - a mask of misiing values in _train_data.
+    //                  _missing_mask = 0 means that there are no missing
+    //                  values.
+    // params         - parameters of GTB algorithm.
+    // OUTPUT
+    // RESULT
+    */
+    CvGBTrees( const CvMat* _train_data, int _tflag,
+             const CvMat* _responses, const CvMat* _var_idx=0,
+             const CvMat* _sample_idx=0, const CvMat* _var_type=0,
+             const CvMat* _missing_mask=0,
+             CvGBTreesParams params=CvGBTreesParams() );
+
+             
+    /*
+    // Destructor.
+    */
+    virtual ~CvGBTrees();
+    
+    
+    /*
+    // Gradient tree boosting model training
+    //
+    // API
+    // virtual bool train( const CvMat* _train_data, int _tflag,
+             const CvMat* _responses, const CvMat* _var_idx=0,
+             const CvMat* _sample_idx=0, const CvMat* _var_type=0,
+             const CvMat* _missing_mask=0,
+             CvGBTreesParams params=CvGBTreesParams(),
+             bool update=false );
+    
+    // INPUT
+    // _train_data    - a set of input feature vectors.
+    //                  size of matrix is
+    //                  <count of samples> x <variables count>
+    //                  or <variables count> x <count of samples>
+    //                  depending on the _tflag parameter.
+    //                  matrix values are float.
+    // _tflag         - a flag showing how do samples stored in the
+    //                  _train_data matrix row by row (tflag=CV_ROW_SAMPLE)
+    //                  or column by column (tflag=CV_COL_SAMPLE).
+    // _responses     - a vector of responses corresponding to the samples
+    //                  in _train_data.
+    // _var_idx       - indices of used variables. zero value means that all
+    //                  variables are active.
+    // _sample_idx    - indices of used samples. zero value means that all
+    //                  samples from _train_data are in the training set.
+    // _var_type      - vector of <variables count> length. gives every
+    //                  variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
+    //                  _var_type = 0 means all variables are numerical.
+    // _missing_mask  - a mask of misiing values in _train_data.
+    //                  _missing_mask = 0 means that there are no missing
+    //                  values.
+    // params         - parameters of GTB algorithm.
+    // update         - is not supported now. (!)
+    // OUTPUT
+    // RESULT
+    // Error state.
+    */
+    virtual bool train( const CvMat* _train_data, int _tflag,
+             const CvMat* _responses, const CvMat* _var_idx=0,
+             const CvMat* _sample_idx=0, const CvMat* _var_type=0,
+             const CvMat* _missing_mask=0,
+             CvGBTreesParams params=CvGBTreesParams(),
+             bool update=false );
+    
+    
+    /*
+    // Gradient tree boosting model training
+    //
+    // API
+    // virtual bool train( CvMLData* data,
+             CvGBTreesParams params=CvGBTreesParams(),
+             bool update=false ) {return false;};
+    
+    // INPUT
+    // data          - training set.
+    // params        - parameters of GTB algorithm.
+    // update        - is not supported now. (!)
+    // OUTPUT
+    // RESULT
+    // Error state.
+    */
+    virtual bool train( CvMLData* data,
+             CvGBTreesParams params=CvGBTreesParams(),
+             bool update=false );
+
+    
+    /*
+    // Response value prediction
+    //
+    // API
+    // virtual float predict( const CvMat* _sample, const CvMat* _missing=0,
+             CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
+             int k=-1 ) const;
+    
+    // INPUT
+    // _sample         - input sample of the same type as in the training set.
+    // _missing        - missing values mask. _missing=0 if there are no
+    //                   missing values in _sample vector.
+    // weak_responses  - predictions of all of the trees.
+    //                   not implemented (!)
+    // slice           - part of the ensemble used for prediction.
+    //                   slice = CV_WHOLE_SEQ when all trees are used.
+    // k               - number of ensemble used.
+    //                   k is in {-1,0,1,..,<count of output classes-1>}.
+    //                   in the case of classification problem 
+    //                   <count of output classes-1> ensembles are built.
+    //                   If k = -1 ordinary prediction is the result,
+    //                   otherwise function gives the prediction of the
+    //                   k-th ensemble only.
+    // OUTPUT
+    // RESULT
+    // Predicted value.
+    */
+    virtual float predict( const CvMat* _sample, const CvMat* _missing=0,
+            CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
+            int k=-1 ) const;
+
+    /*
+    // Delete all temporary data.
+    //
+    // API
+    // virtual void clear();
+    
+    // INPUT
+    // OUTPUT
+    // delete data, weak, orig_response, sum_response,
+    //        weak_eval, ubsample_train, subsample_test,
+    //        sample_idx, missing, lass_labels
+    // delta = 0.0
+    // RESULT
+    */
+    virtual void clear();
+
+    /*
+    // Compute error on the train/test set.
+    //
+    // API
+    // virtual float calc_error( CvMLData* _data, int type,
+    //        std::vector<float> *resp = 0 );
+    //
+    // INPUT
+    // data  - dataset
+    // type  - defines which error is to compute^ train (CV_TRAIN_ERROR) or
+    //         test (CV_TEST_ERROR).
+    // OUTPUT
+    // resp  - vector of predicitons
+    // RESULT
+    // Error value.
+    */
+    virtual float calc_error( CvMLData* _data, int type,
+            std::vector<float> *resp = 0 );
+
+
+    /*
+    // 
+    // Write parameters of the gtb model and data. Write learned model.
+    //
+    // API
+    // virtual void write( CvFileStorage* fs, const char* name ) const;
+    //
+    // INPUT
+    // fs     - file storage to read parameters from.
+    // name   - model name.
+    // OUTPUT
+    // RESULT
+    */
+    virtual void write( CvFileStorage* fs, const char* name ) const;
+
+
+    /*
+    // 
+    // Read parameters of the gtb model and data. Read learned model.
+    //
+    // API
+    // virtual void read( CvFileStorage* fs, CvFileNode* node );
+    //
+    // INPUT
+    // fs     - file storage to read parameters from.
+    // node   - file node.
+    // OUTPUT
+    // RESULT
+    */
+    virtual void read( CvFileStorage* fs, CvFileNode* node );
+
+
+protected:
+
+    /*
+    // Compute the gradient vector components.
+    //
+    // API
+    // virtual void find_gradient( const int k = 0);
+    
+    // INPUT
+    // k        - used for classification problem, determining current
+    //            tree ensemble.
+    // OUTPUT
+    // changes components of data->responses
+    // which correspond to samples used for training
+    // on the current step.
+    // RESULT
+    */
+    virtual void find_gradient( const int k = 0);
+
+    
+    /*
+    // 
+    // Change values in tree leaves according to the used loss function.
+    //
+    // API
+    // virtual void change_values(CvDTree* tree, const int k = 0);
+    //
+    // INPUT
+    // tree      - decision tree to change.
+    // k         - used for classification problem, determining current
+    //             tree ensemble.
+    // OUTPUT
+    // changes 'value' fields of the trees' leaves.
+    // changes sum_response_tmp.
+    // RESULT
+    */
+    virtual void change_values(CvDTree* tree, const int k = 0);
+
+
+    /*
+    // 
+    // Find optimal constant prediction value according to the used loss
+    // function.
+    // The goal is to find a constant which gives the minimal summary loss
+    // on the _Idx samples.
+    //
+    // API
+    // virtual float find_optimal_value( const CvMat* _Idx );
+    //
+    // INPUT
+    // _Idx        - indices of the samples from the training set.
+    // OUTPUT
+    // RESULT
+    // optimal constant value.
+    */
+    virtual float find_optimal_value( const CvMat* _Idx );
+
+    
+    /*
+    // 
+    // Randomly split the whole training set in two parts according
+    // to params.portion.
+    //
+    // API
+    // virtual void do_subsample();
+    //
+    // INPUT
+    // OUTPUT
+    // subsample_train - indices of samples used for training
+    // subsample_test  - indices of samples used for test
+    // RESULT
+    */
+    virtual void do_subsample();
+
+
+    /*
+    // 
+    // Internal recursive function giving an array of subtree tree leaves.
+    //
+    // API
+    // void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
+    //
+    // INPUT
+    // node         - current leaf.
+    // OUTPUT
+    // count        - count of leaves in the subtree.
+    // leaves       - array of pointers to leaves.
+    // RESULT
+    */
+    void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
+    
+    
+    /*
+    // 
+    // Get leaves of the tree.
+    //
+    // API
+    // CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
+    //
+    // INPUT
+    // dtree            - decision tree.
+    // OUTPUT
+    // len              - count of the leaves.
+    // RESULT
+    // CvDTreeNode**    - array of pointers to leaves.
+    */
+    CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
+
+    
+    /*
+    // 
+    // Is it a regression or a classification.
+    //
+    // API
+    // bool problem_type();
+    //
+    // INPUT
+    // OUTPUT
+    // RESULT
+    // false if it is a classification problem,
+    // true - if regression.
+    */
+    virtual bool problem_type() const;
+
+
+    /*
+    // 
+    // Write parameters of the gtb model.
+    //
+    // API
+    // virtual void write_params( CvFileStorage* fs ) const;
+    //
+    // INPUT
+    // fs           - file storage to write parameters to.
+    // OUTPUT
+    // RESULT
+    */
+    virtual void write_params( CvFileStorage* fs ) const;
+
+
+    /*
+    // 
+    // Read parameters of the gtb model and data.
+    //
+    // API
+    // virtual void read_params( CvFileStorage* fs );
+    //
+    // INPUT
+    // fs           - file storage to read parameters from.
+    // OUTPUT
+    // params       - parameters of the gtb model.
+    // data         - contains information about the structure
+    //                of the data set (count of variables,
+    //                their types, etc.).
+    // class_labels - output class labels map.
+    // RESULT
+    */
+    virtual void read_params( CvFileStorage* fs, CvFileNode* fnode );
+
+    
+    CvDTreeTrainData* data;
+    CvGBTreesParams params;
+
+    CvSeq** weak;
+    CvMat* orig_response;
+    CvMat* sum_response;
+    CvMat* sum_response_tmp;
+    CvMat* weak_eval;
+    CvMat* sample_idx;
+    CvMat* subsample_train;
+    CvMat* subsample_test;
+    CvMat* missing;
+    CvMat* class_labels;
+
+    CvRNG rng;
+
+    int class_count;
+    float delta;
+    float base_value;
+
+};
+
+
+
 /****************************************************************************************\
 *                              Artificial Neural Networks (ANN)                          *
 \****************************************************************************************/
@@ -1936,6 +2463,8 @@ typedef CvBoostTree BoostTree;
 typedef CvBoost Boost;
 typedef CvANN_MLP_TrainParams ANN_MLP_TrainParams;
 typedef CvANN_MLP NeuralNet_MLP;
+typedef CvGBTreesParams GradientBoostingTreesParams;
+typedef CvGBTrees GradientBoostingTrees;
     
 }
 

tests/ml/src/gbttest.cpp

@@ -0,0 +1,271 @@
+
+#include "mltest.h"
+#include <string>
+#include <fstream>
+#include <iostream>
+
+using namespace std;
+
+
+class CV_GBTreesTest : public CvTest
+{
+public:
+    CV_GBTreesTest();
+    ~CV_GBTreesTest(); 
+    
+protected:
+    void run(int);
+
+    int TestTrainPredict(int test_num);
+    int TestSaveLoad();
+
+    int checkPredictError(int test_num);
+    int checkLoadSave();  
+    
+    //string model_file_name1;
+    //string model_file_name2;
+    char model_file_name1[50];
+    char model_file_name2[50];
+    string* datasets;
+    string data_path;
+    
+    CvMLData* data;
+    CvGBTrees* gtb;
+    
+    vector<float> test_resps1;
+    vector<float> test_resps2;
+};
+
+
+int _get_len(const CvMat* mat)
+{
+    return (mat->cols > mat->rows) ? mat->cols : mat->rows;
+}
+
+
+CV_GBTreesTest::CV_GBTreesTest() :
+                CvTest( "CvGBTrees_test",
+                        "all public methods (train, predict, save, load)" )
+{
+    datasets = 0;
+    data = 0;
+    gtb = 0;
+}
+
+CV_GBTreesTest::~CV_GBTreesTest()
+{
+    if (data)
+        delete data;
+    delete[] datasets;
+}
+
+
+int CV_GBTreesTest::TestTrainPredict(int test_num)
+{
+    int code = CvTS::OK;
+    
+    int weak_count = 200;
+    float shrinkage = 0.1f;
+    float subsample_portion = 0.5f;
+    int max_depth = 5;
+    bool use_surrogates = true;
+    int loss_function_type = 0;
+    switch (test_num)
+    {
+        case (1) : loss_function_type = CvGBTrees::SQUARED_LOSS; break;
+        case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
+        case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
+        case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
+        default  : 
+            {
+            ts->printf( CvTS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
+            return CvTS::FAIL_BAD_ARG_CHECK;
+            }
+    }
+
+    int dataset_num = test_num == 0 ? 0 : 1;
+    if (!data)
+    {
+        data = new CvMLData();
+        data->set_delimiter(',');
+        
+        if (data->read_csv(datasets[dataset_num].c_str()))
+        {
+            ts->printf( CvTS::LOG, "File reading error." );
+            return CvTS::FAIL_INVALID_TEST_DATA;
+        }
+
+        if (test_num == 0)
+        {
+            data->set_response_idx(57);
+            data->set_var_types("ord[0-56],cat[57]");
+        }
+        else
+        {
+            data->set_response_idx(13);
+            data->set_var_types("ord[0-2,4-13],cat[3]");
+            subsample_portion = 0.7f;
+        }
+
+        int train_sample_count = cvFloor(_get_len(data->get_responses())*0.5f);
+        CvTrainTestSplit spl( train_sample_count );
+        data->set_train_test_split( &spl );
+    }
+    
+    data->mix_train_and_test_idx();    
+    
+    
+    if (gtb) delete gtb;
+    gtb = new CvGBTrees();
+    bool tmp_code = true;
+    tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
+                          shrinkage, subsample_portion,
+                          max_depth, use_surrogates));
+    
+    if (!tmp_code)
+    {
+        ts->printf( CvTS::LOG, "Model training was failed.");
+        return CvTS::FAIL_INVALID_OUTPUT;
+    }
+    
+    code = checkPredictError(test_num);
+    
+    return code;
+
+}
+
+
+int CV_GBTreesTest::checkPredictError(int test_num)
+{
+    if (!gtb)
+        return CvTS::FAIL_GENERIC;
+        
+    float mean[] = {5.3555f, 11.2241f, 11.9212f, 12.0848f};
+    float sigma[] = {0.362127f, 3.4906f, 3.4906f, 3.64994f};
+    
+    float current_error = gtb->calc_error(data, CV_TEST_ERROR);
+    
+    if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] )
+    {
+        ts->printf( CvTS::LOG, "Test error is out of range:\n"
+                    "abs(%f/*curEr*/ - %f/*mean*/ > %f/*6*sigma*/",
+                    current_error, mean[test_num], 6*sigma[test_num] );
+        return CvTS::FAIL_BAD_ACCURACY;
+    }
+
+    return CvTS::OK;
+
+}
+
+
+int CV_GBTreesTest::TestSaveLoad()
+{
+    if (!gtb)
+        return CvTS::FAIL_GENERIC;
+        
+    tmpnam(model_file_name1);
+    tmpnam(model_file_name2);
+
+    gtb->save(model_file_name1);
+    gtb->calc_error(data, CV_TEST_ERROR, &test_resps1);
+    gtb->load(model_file_name1);
+    gtb->calc_error(data, CV_TEST_ERROR, &test_resps2);
+    gtb->save(model_file_name2);
+    
+    return checkLoadSave();
+    
+}
+
+
+
+int CV_GBTreesTest::checkLoadSave()
+{
+    int code = CvTS::OK;
+
+    // 1. compare files
+    ifstream f1( model_file_name1 ), f2( model_file_name2 );
+    string s1, s2;
+    int lineIdx = 0; 
+    CV_Assert( f1.is_open() && f2.is_open() );
+    for( ; !f1.eof() && !f2.eof(); lineIdx++ )
+    {
+        getline( f1, s1 );
+        getline( f2, s2 );
+        if( s1.compare(s2) )
+        {
+            ts->printf( CvTS::LOG, "first and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
+               lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
+            code = CvTS::FAIL_INVALID_OUTPUT;
+        }
+    }
+    if( !f1.eof() || !f2.eof() )
+    {
+        ts->printf( CvTS::LOG, "First and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
+            lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
+        code = CvTS::FAIL_INVALID_OUTPUT;
+    }
+    f1.close();
+    f2.close();
+    // delete temporary files
+    remove( model_file_name1 );
+    remove( model_file_name2 );
+
+    // 2. compare responses
+    CV_Assert( test_resps1.size() == test_resps2.size() );
+    vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
+    for( ; it1 != test_resps1.end(); ++it1, ++it2 )
+    {
+        if( fabs(*it1 - *it2) > FLT_EPSILON )
+        {
+            ts->printf( CvTS::LOG, "Responses predicted before saving and after loading are different" );
+            code = CvTS::FAIL_INVALID_OUTPUT;
+        }
+    }
+    return code;
+}
+
+
+
+void CV_GBTreesTest::run(int)
+{
+
+    string data_path = string(ts->get_data_path());
+    datasets = new string[2];
+    datasets[0] = data_path + string("spambase.data"); /*string("dataset_classification.csv");*/
+    datasets[1] = data_path + string("housing_.data");  /*string("dataset_regression.csv");*/
+
+    int code = CvTS::OK;
+
+    for (int i = 0; i < 4; i++)
+    {
+    
+        int temp_code = TestTrainPredict(i);
+        if (temp_code != CvTS::OK)
+        {
+            code = temp_code;
+            break;
+        }
+            
+        else if (i==0)
+        {
+            temp_code = TestSaveLoad();
+            if (temp_code != CvTS::OK)
+                code = temp_code;
+            delete data;
+            data = 0;
+        }
+        
+        delete gtb;
+        gtb = 0;
+    }
+    delete data;
+    data = 0;
+    
+    ts->set_failed_test_info( code );
+}
+
+/////////////////////////////////////////////////////////////////////////////
+//////////////////// test registration  /////////////////////////////////////
+/////////////////////////////////////////////////////////////////////////////
+
+CV_GBTreesTest gbtrees_test;