class_docstr: Streaming interface to allow for any source of predictions. 
  Initialize it, count predictions one by one, then print confusion matrix and intersection-union score
class_docstr_tok: ["Streaming","interface","to","allow","for","any","source","of","predictions",".","Initialize","it",",","count","predictions","one","by","one",",","then","print","confusion","matrix","and","intersection-union","score"]
code: def get_mean_class_accuracy(self):  # added
      re = 0
      label_presents = 0
      for i in range(self.number_of_labels):
          total_gt = np.sum(self.confusion_matrix[i, :])
          if total_gt:
              label_presents += 1
              re = re + self.confusion_matrix[i][i] / max(1, total_gt)
      if label_presents == 0:
          return 0
      return re / label_presents
  
file_path: torch-points3d/torch_points3d/metrics/confusion_matrix.py
imports: import numpy
  import torch
  import os
label_desc: Use image node_id 9 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.get_mean_class_accuracy() and returns: unknown, int