class_docstr: Class implementing the Learner in the FairFictPlay algorithm for rich subgroup fairness in [KRNW18].
      
docstr: Return the classifications of the average classifier at time iter.
  
  Args:
      :param q: the most recent classifier found
      :param predictions: the previous set of decisions (probabilities) up to time iter - 1
      :param iteration: the number of the current iteration
  
  Returns:
      :return error: the error of the average classifier found thus far (incorporating q)
docstr_tok: ["Return","the","classifications","of","the","average","classifier","at","time","iter",".","Args",":",":","param","q",":","the","most","recent","classifier","found",":","param","predictions",":","the","previous","set","of","decisions","(","probabilities",")","up","to","time","iter","-","1",":","param","iteration",":","the","number","of","the","current","iteration","Returns",":",":","return","error",":","the","error","of","the","average","classifier","found","thus","far","(","incorporating","q",")"]
code: def generate_predictions(self, q, predictions, iteration):
      """Return the classifications of the average classifier at time iter.
  
      Args:
          :param q: the most recent classifier found
          :param predictions: the previous set of decisions (probabilities) up to time iter - 1
          :param iteration: the number of the current iteration
  
      Returns:
          :return error: the error of the average classifier found thus far (incorporating q)
      """
  
      new_predictions = np.multiply(1.0 / iteration, q.predict(self.X))
      ds = np.multiply((iteration - 1.0) / iteration, predictions)
      ds += new_predictions
      error = np.mean(
          [np.abs(ds[k] - self.y[k]) for k in range(len(self.y))]
      )
      ds = tuple(ds)
      return error, ds
  
imports: import numpy
  import copy
  from aif360.algorithms.inprocessing.gerryfair.reg_oracle_class import RegOracle
label_desc: Use image node_id 3 for calling the Learner obj's underlying member method code with example usage: obj.generate_predictions(q, predictions, iteration) and returns: error,  ds