class_docstr: A stopping criterion which stops as soon as the average overall uncertainty falls
  below a given threshold [ZWH08]_.
  
  As a measure of uncertainty, normalized prediction entropy is used. In order to reproduce
  the original implementation pass the unlabeled set `indices_stopping` to `stop()` method.
  
  .. versionadded:: 1.1.0
docstr: num_classes : int
      Number of classes.
  threshold : float, default=0.05
      A normalized entropy value below which the criterion indicates to stop.
docstr_tok: ["num_classes",":","int","Number","of","classes",".","threshold",":","float",",","default=0.05","A","normalized","entropy","value","below","which","the","criterion","indicates","to","stop","."]
code: def __init__(self, num_classes, threshold=0.05):
      """
      num_classes : int
          Number of classes.
      threshold : float, default=0.05
          A normalized entropy value below which the criterion indicates to stop.
      """
      if threshold < 0 or threshold >= 1:
          raise ValueError(
              f"Threshold must be between 0 (inclusive) and 1 (exclusive), "
              f"but got {threshold}."
          )
  
      self.num_classes = num_classes
      self.threshold = threshold
  
      self.last_predictions = None
  
imports: import numpy
  from scipy.stats import entropy
  from small_text.stopping_criteria.base import StoppingCriterion
label_desc: Use image node_id 1 to create a new OverallUncertainty object from inherited base classes: StoppingCriterion with example: obj = OverallUncertainty(num_classes, threshold)