class_docstr: Base class for evaluating methods for optimisation. 
class_docstr_tok: ["Base","class","for","evaluating","methods","for","optimisation","."]
docstr: Updates the results of the data to be saved with curr_iter_results.
docstr_tok: ["Updates","the","results","of","the","data","to","be","saved","with","curr_iter_results","."]
code: def update_to_be_saved(self, curr_iter_results):
      """Updates the results of the data to be saved with curr_iter_results."""
      for data_type in (
          self.data_to_be_saved + self.data_to_be_saved_if_available
      ):
          data = getattr(curr_iter_results, data_type)
          curr_data_to_be_saved = getattr(self.to_be_saved, data_type)
          if curr_data_to_be_saved.shape[1] == self.trial_iter:
              updated_data_to_be_saved = curr_data_to_be_saved
              updated_data_to_be_saved[:, -1] = data.ravel()
          elif curr_data_to_be_saved.shape[1] < self.trial_iter:
              updated_data_to_be_saved = np.concatenate(
                  (curr_data_to_be_saved, data), axis=1
              )
          else:
              raise ValueError("Something wrong with data saving.")
          setattr(self.to_be_saved, data_type, updated_data_to_be_saved)
  
imports: from argparse import Namespace
  from datetime import datetime
  import os
  import numpy
  from .blackbox_optimiser import OptInitialiser
  from ..utils.method_evaluator import BaseMethodEvaluator
label_desc: Use image node_id 13 for calling the OptMethodEvaluator obj's underlying member method code with example usage: obj.update_to_be_saved(curr_iter_results) without return types