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 and its derivative
docstr_tok: ["Integrated","GP-Lower","Confidence","Bound","and","its","derivative"]
code: def _compute_acq_withGradients(self, x):
      """
      Integrated GP-Lower Confidence Bound and its derivative
      """
      means, stds, dmdxs, dsdxs = self.model.predict_withGradients(x)
      f_acqu = None
      df_acqu = None
      for m, s, dmdx, dsdx in zip(means, stds, dmdxs, dsdxs):
          f = -m + self.exploration_weight * s
          df = -dmdx + self.exploration_weight * dsdx
          if f_acqu is None:
              f_acqu = f
              df_acqu = df
          else:
              f_acqu += f
              df_acqu += df
      return f_acqu / (len(means)), df_acqu / (len(means))
  
imports: from .LCB import AcquisitionLCB
label_desc: Use image node_id 3 for calling the AcquisitionLCB_MCMC obj's underlying member method code with example usage: obj._compute_acq_withGradients(x) and returns: unknown,  unknown