class_docstr: Integrated GP-Lower Confidence Bound acquisition function
  
  :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 exploration_weight: positive parameter to control exploration / exploitation
  
  .. Note:: allows to compute the Improvement per unit of cost
class_docstr_tok: ["Integrated","GP-Lower","Confidence","Bound","acquisition","function",":","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","exploration_weight",":","positive","parameter","to","control","exploration","\/","exploitation","..","Note",":",":","allows","to","compute","the","Improvement","per","unit","of","cost"]
docstr: Integrated GP-Lower Confidence Bound
docstr_tok: ["Integrated","GP-Lower","Confidence","Bound"]
code: def _compute_acq(self, x):
      """
      Integrated GP-Lower Confidence Bound
      """
      means, stds = self.model.predict(x)
      f_acqu = 0
      for m, s in zip(means, stds):
          f_acqu += -m + self.exploration_weight * s
      return f_acqu / (len(means))
  
imports: from .LCB import AcquisitionLCB
label_desc: Use image node_id 2 for calling the AcquisitionLCB_MCMC obj's underlying member method code with example usage: obj._compute_acq(x) and returns: unknown