class_docstr: Blackbox Optimiser Class. 
class_docstr_tok: ["Blackbox","Optimiser","Class","."]
docstr: Set up for black-box optimisation. 
docstr_tok: ["Set","up","for","black-box","optimisation","."]
code: def _moo_set_up(self):
      """Set up for black-box optimisation."""
      # Initialise optimal values and points
      # (Optimal point for MF problems is not defined)
      # (Instead a set of pareto optimal points will be maintained)
      self.curr_pareto_vals = []
      self.curr_pareto_points = []
      self.curr_true_pareto_vals = []
      self.curr_true_pareto_points = []
      # Set up history
      self.history.query_vals = []
      self.history.query_true_vals = []
      self.history.curr_pareto_vals = []
      self.history.curr_pareto_points = []
      self.history.curr_true_pareto_vals = []
      self.history.curr_true_pareto_points = []
      if self.multi_func_caller.is_mf():
          # self.history.query_at_fidel_to_opts = []
          raise NotImplementedError(_NO_MF_FOR_MOO_ERR_MSG)
      # Set up attributes to be copied from history
      self.to_copy_from_qinfo_to_history["val"] = "query_vals"
      self.to_copy_from_qinfo_to_history["true_val"] = "query_true_vals"
      # Set up previous evaluations
      self.prev_eval_vals = []
      self.prev_eval_true_vals = []
      self.history.prev_eval_vals = self.prev_eval_vals
      self.history.prev_eval_true_vals = self.prev_eval_true_vals
  
imports: from __future__ import division
  from argparse import Namespace
  import numpy
  from ..exd.exd_core import ExperimentDesigner, exd_core_args
  from ..exd.experiment_caller import MultiFunctionCaller, FunctionCaller
  from ..exd.exd_utils import EVAL_ERROR_CODE
  from ..utils.general_utils import update_pareto_set
label_desc: Use image node_id 3 for calling the MultiObjectiveOptimiser obj's underlying member method code with example usage: obj._moo_set_up() without return types