76             avg.resize(nclasses);  in train()
85                 avg[cls]              = Mat::zeros( 1, nvars, CV_64FC1 );  in train()
131         // calculate avg, covariance matrix, c  in train()
138             double* avg_data = avg[cls].ptr<double>();  in train()
150             avg_data = avg[cls].ptr<double>();  in train()
155                 double* avg2_data = avg[cls].ptr<double>();  in train()
199             avg = &_avg;  in NBPredictBody()
211         const vector<Mat>* avg;  member in cv::ml::NormalBayesClassifierImpl::NBPredictBody
227             int nvars = avg->at(0).cols;  in operator ()()
258                     const double* avg_data = avg->at(i).ptr<double>();  in operator ()()
335                           NBPredictBody(c, cov_rotate_mats, inv_eigen_values, avg, samples,  in predictProb()
364         fs << "]" << "avg" << "[";  in write()
366             fs << avg[i];  in write()
403             avg_it = fn["avg"].begin(),  in read()
410         avg.resize(nclasses);  in read()
420             *avg_it >> avg[i];  in read()
433         avg.clear();  in clear()
443     bool isTrained() const { return !avg.empty(); }  in isTrained()
450     vector<Mat> count, sum, productsum, avg, inv_eigen_values, cov_rotate_mats;  member in cv::ml::NormalBayesClassifierImpl