brief gbt documentation added. some sample fixes made. code updated.

modules/ml/doc/ml.rst

@@ -15,7 +15,7 @@ Most of the classification and regression algorithms are implemented as C++ clas
     support_vector_machines
     decision_trees
     boosting
+    gradient_boosted_trees
     random_trees
     expectation_maximization
     neural_networks
-

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

@@ -1571,7 +1571,7 @@ public:
     // Response value prediction
     //
     // API
-    // virtual float predict( const CvMat* sample, const CvMat* missing=0,
+    // virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
              CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
              int k=-1 ) const;
     
@@ -1594,12 +1594,44 @@ public:
     // RESULT
     // Predicted value.
     */
+    virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
+            CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
+            int k=-1 ) const;
+            
+    /*
+    // Response value prediction.
+    // Parallel version (in the case of TBB existence)
+    //
+    // 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* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
             int k=-1 ) const;
 
     /*
-    // Delete all temporary data.
+    // Deletes all the data.
     //
     // API
     // virtual void clear();
@@ -1607,7 +1639,7 @@ public:
     // INPUT
     // OUTPUT
     // delete data, weak, orig_response, sum_response,
-    //        weak_eval, ubsample_train, subsample_test,
+    //        weak_eval, subsample_train, subsample_test,
     //        sample_idx, missing, lass_labels
     // delta = 0.0
     // RESULT
@@ -1623,7 +1655,7 @@ public:
     //
     // INPUT
     // data  - dataset
-    // type  - defines which error is to compute^ train (CV_TRAIN_ERROR) or
+    // type  - defines which error is to compute: train (CV_TRAIN_ERROR) or
     //         test (CV_TEST_ERROR).
     // OUTPUT
     // resp  - vector of predicitons
@@ -1633,7 +1665,6 @@ public:
     virtual float calc_error( CvMLData* _data, int type,
             std::vector<float> *resp = 0 );
 
-
     /*
     // 
     // Write parameters of the gtb model and data. Write learned model.
@@ -1852,7 +1883,6 @@ protected:
     CvMat* orig_response;
     CvMat* sum_response;
     CvMat* sum_response_tmp;
-    CvMat* weak_eval;
     CvMat* sample_idx;
     CvMat* subsample_train;
     CvMat* subsample_test;

samples/c/tree_engine.cpp

@@ -125,7 +125,10 @@ int main(int argc, char** argv)
         print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
 
         printf("======GBTREES=====\n");
-        gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.05f, 0.6f, 10, true));
+		if (categorical_response)
+			gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.1f, 0.8f, 5, false));
+		else
+			gbtrees.train( &data, CvGBTreesParams(CvGBTrees::SQUARED_LOSS, 100, 0.1f, 0.8f, 5, false));
         print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
     }
     else