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"]
docstr: returns 64-bit float
docstr_tok: ["returns","64-bit","float"]
code: def get_overall_accuracy(self):
      """returns 64-bit float"""
      confusion_matrix = self.confusion_matrix
      matrix_diagonal = 0
      all_values = 0
      for row in range(self.number_of_labels):
          for column in range(self.number_of_labels):
              all_values += confusion_matrix[row][column]
              if row == column:
                  matrix_diagonal += confusion_matrix[row][column]
      if all_values == 0:
          all_values = 1
      return float(matrix_diagonal) / all_values
  
file_path: torch-points3d/torch_points3d/metrics/confusion_matrix.py
imports: import numpy
  import torch
  import os
label_desc: Use image node_id 7 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.get_overall_accuracy() and returns: unknown