class_docstr: Abstract base class for early time series classifiers.
  
  The base classifier specifies the methods and method signatures that all
  early classifiers have to implement. Attributes with an underscore suffix are set in
  the method fit.
  
  Parameters
  ----------
  classes_            : ndarray of class labels, possibly strings
  n_classes_          : integer, number of classes (length of classes_)
  fit_time_           : integer, time (in milliseconds) for fit to run.
  _class_dictionary   : dictionary mapping classes_ onto integers 0...n_classes_-1.
  _threads_to_use     : number of threads to use in fit as determined by n_jobs.
  state_info          : An array containing the state info for each decision in X.
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code: def __init__(self):
      self.classes_ = []
      self.n_classes_ = 0
      self.fit_time_ = 0
      self._class_dictionary = {}
      self._threads_to_use = 1
      """An array containing the state info for each decision in X from update and
          predict methods.
  
          Contains classifier dependent information for future decisions on the data and
          information on when a cases decision has been made. Each row contains
          information for a case from the latest decision on its safety made in
          update/predict. Successive updates are likely to remove rows from the
          state_info, as it will only store as many rows as there are input instances to
          update/predict.
          """
      self.state_info = None
  
      self._converter_store_y = {}
  
      super().__init__()
  
imports: from abc import ABC, abstractmethod
  from typing import Tuple
  import numpy
  from sktime.base import BaseEstimator
  from sktime.classification import BaseClassifier
label_desc: Use image node_id 1 to create a new BaseEarlyClassifier object from inherited base classes: BaseEstimator, ABC with example: obj = BaseEarlyClassifier()