class_docstr: Bayesian Optimiser using `GPyOpt` as a backend.
class_docstr_tok: ["Bayesian","Optimiser","using","`","GPyOpt","`","as","a","backend","."]
docstr: Reset the optimiser for a fresh start.
docstr_tok: ["Reset","the","optimiser","for","a","fresh","start","."]
code: def reset(self):
      """Reset the optimiser for a fresh start."""
      super(BayesianOptimisation, self).reset()
      self._data_x = np.array([])
      self._data_fx = np.array([])
      self.__is_empty_data = True
  
imports: from multiprocessing import cpu_count
  from typing import Any, Dict, List, Sequence, Tuple, Type, TypeVar, Union
  import GPy
  import GPyOpt
  import numpy
  from GPyOpt.core import errors
  from hypertunity import utils
  from hypertunity.domain import Domain, Sample
  from hypertunity.optimisation.base import EvaluationScore, ExhaustedSearchSpaceError, Optimiser
label_desc: Use image node_id 9 for calling the BayesianOptimisation obj's underlying member method code with example usage: obj.reset() without return types