88     const CvMat* _train_data, const CvMat* _responses,  in CvNormalBayesClassifier()
89     const CvMat* _var_idx, const CvMat* _sample_idx )  in CvNormalBayesClassifier()
107 bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses,  in train()
108                             const CvMat* _var_idx, const CvMat* _sample_idx, bool update )  in train()
112     CvMat* responses   = 0;  in train()
114     CvMat* __cls_labels = 0;  in train()
115     CvMat* __var_idx = 0;  in train()
116     CvMat* cov = 0;  in train()
134         const size_t mat_size = sizeof(CvMat*);  in train()
148         CV_CALL( count = (CvMat**)cvAlloc( data_size ));  in train()
219         CvMat* w = inv_eigen_values[cls];  in train()
281 float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) const  in predict()
293     CvMat diff;  in predict()
343             CvMat* u = cov_rotate_mats[i];  in predict()
344             CvMat* w = inv_eigen_values[i];  in predict()
471     CV_CALL( var_idx = (CvMat*)cvReadByName( fs, root_node, "var_idx" ));  in read()
472     CV_CALL( cls_labels = (CvMat*)cvReadByName( fs, root_node, "cls_labels" ));  in read()
482     data_size = nclasses*6*sizeof(CvMat*);  in read()
483     CV_CALL( count = (CvMat**)cvAlloc( data_size ));  in read()
499         CV_CALL( count[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));  in read()
510         CV_CALL( sum[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));  in read()
521         CV_CALL( productsum[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));  in read()
532         CV_CALL( avg[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));  in read()
543         CV_CALL( inv_eigen_values[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));  in read()
554         CV_CALL( cov_rotate_mats[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));  in read()
558     CV_CALL( c = (CvMat*)cvReadByName( fs, root_node, "c" ));  in read()