class_docstr: GP-Lower Confidence Bound acquisition function with constant exploration weight.
  See:
  
  Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
  Srinivas et al., Proc. International Conference on Machine Learning (ICML), 2010
  
  :param model: GPyOpt class of model
  :param space: GPyOpt class of domain
  :param optimizer: optimizer of the acquisition. Should be a GPyOpt optimizer
  :param cost_withGradients: function
  :param jitter: positive value to make the acquisition more explorative
  
  .. Note:: does not allow to be used with cost
code: def __init__(
      self,
      model,
      space,
      optimizer=None,
      cost_withGradients=None,
      exploration_weight=2,
  ):
      self.optimizer = optimizer
      super(AcquisitionLCB, self).__init__(model, space, optimizer)
      self.exploration_weight = exploration_weight
  
      if cost_withGradients is not None:
          print(
              "The set cost function is ignored! LCB acquisition does not make sense with cost."
          )
  
imports: from .base import AcquisitionBase
  from ..util.general import get_quantiles
label_desc: Use image node_id 1 to create a new AcquisitionLCB object from inherited base classes: AcquisitionBase with example: obj = AcquisitionLCB(model, space, optimizer, cost_withGradients, exploration_weight)