class_docstr: Custom early time series classifier. todo: write docstring.
  
  todo: describe your custom early time series classifier here
  
  Hyper-parameters
  ----------------
  parama : int
      descriptive explanation of parama
  paramb : string, optional (default='default')
      descriptive explanation of paramb
  paramc : boolean, optional (default= whether paramb is not the default)
      descriptive explanation of paramc
  and so on
  
  Components
  ----------
  est : sktime.estimator, BaseEstimator descendant
      descriptive explanation of est
  est2: another estimator
      descriptive explanation of est2
  and so on
class_docstr_tok: ["Custom","early","time","series","classifier",".","todo",":","write","docstring",".","todo",":","describe","your","custom","early","time","series","classifier","here","Hyper-parameters","--","--","--","--","--","--","--","--","parama",":","int","descriptive","explanation","of","parama","paramb",":","string",",","optional","(","default='default","'",")","descriptive","explanation","of","paramb","paramc",":","boolean",",","optional","(","default=","whether","paramb","is","not","the","default",")","descriptive","explanation","of","paramc","and","so","on","Components","--","--","--","--","--","est",":","sktime.estimator",",","BaseEstimator","descendant","descriptive","explanation","of","est","est2",":","another","estimator","descriptive","explanation","of","est2","and","so","on"]
docstr: Update labels probabilities for sequences in X using a larger series length.
  
  Uses information from previous decisions stored in state_info. This method
  should update state_info with any values necessary to make future decisions.
  It is recommended that the previous time stamp used for each case should be
  stored in the state_info. The number of rows in state_info after the method has
  been called should match the number of input rows.
  
  Default behaviour is to call _predict and set the predicted class probability
  to 1, other class probabilities to 0. Override if better estimates are
  obtainable.
  
  Parameters
  ----------
  X : data to predict y with, of type self.get_tag("X_inner_mtype")
  
  Returns
  -------
  y : predictions of probabilities for class values of X, np.ndarray
  decisions : decisions on whether the prediction is sage, np.ndarray
docstr_tok: ["Update","labels","probabilities","for","sequences","in","X","using","a","larger","series","length",".","Uses","information","from","previous","decisions","stored","in","state_info",".","This","method","should","update","state_info","with","any","values","necessary","to","make","future","decisions",".","It","is","recommended","that","the","previous","time","stamp","used","for","each","case","should","be","stored","in","the","state_info",".","The","number","of","rows","in","state_info","after","the","method","has","been","called","should","match","the","number","of","input","rows",".","Default","behaviour","is","to","call","_predict","and","set","the","predicted","class","probability","to","1",",","other","class","probabilities","to","0",".","Override","if","better","estimates","are","obtainable",".","Parameters","--","--","--","--","--","X",":","data","to","predict","y","with",",","of","type","self.get_tag","(","``","X_inner_mtype","''",")","Returns","--","--","--","-","y",":","predictions","of","probabilities","for","class","values","of","X",",","np.ndarray","decisions",":","decisions","on","whether","the","prediction","is","sage",",","np.ndarray"]
code: def _update_predict_proba(self, X) -> Tuple[np.ndarray, np.ndarray]:
      """Update labels probabilities for sequences in X using a larger series length.
  
      Uses information from previous decisions stored in state_info. This method
      should update state_info with any values necessary to make future decisions.
      It is recommended that the previous time stamp used for each case should be
      stored in the state_info. The number of rows in state_info after the method has
      been called should match the number of input rows.
  
      Default behaviour is to call _predict and set the predicted class probability
      to 1, other class probabilities to 0. Override if better estimates are
      obtainable.
  
      Parameters
      ----------
      X : data to predict y with, of type self.get_tag("X_inner_mtype")
  
      Returns
      -------
      y : predictions of probabilities for class values of X, np.ndarray
      decisions : decisions on whether the prediction is sage, np.ndarray
      """
  
file_path: sktime/extension_templates/early_classification.py
imports: from typing import Tuple
  import numpy
  from sktime.classification.early_classification import BaseEarlyClassifier
label_desc: Use image node_id 6 for calling the MyEarlyTimeSeriesClassifier obj's underlying member method code with example usage: obj._update_predict_proba(X) without return types