docstr: Update parameters of the acquisition required to evaluate the function. In particular:
      * Sample representer points repr_points
      * Compute their log values repr_points_log
      * Compute belief locations logP
docstr_tok: ["Update","parameters","of","the","acquisition","required","to","evaluate","the","function",".","In","particular",":","*","Sample","representer","points","repr_points","*","Compute","their","log","values","repr_points_log","*","Compute","belief","locations","logP"]
code: def _update_parameters(self):
      """
      Update parameters of the acquisition required to evaluate the function. In particular:
          * Sample representer points repr_points
          * Compute their log values repr_points_log
          * Compute belief locations logP
      """
      self.repr_points, self.repr_points_log = self.sampler.get_samples(
          self.num_repr_points,
          self.proposal_function,
          self.burn_in_steps,
      )
  
      if np.any(np.isnan(self.repr_points_log)) or np.any(
          np.isposinf(self.repr_points_log)
      ):
          raise RuntimeError(
              "Sampler generated representer points with invalid log values: {}".format(
                  self.repr_points_log
              )
          )
  
      # Removing representer points that have 0 probability of being the minimum (corresponding to log probability being minus infinity)
      idx_to_remove = np.where(np.isneginf(self.repr_points_log))[0]
      if len(idx_to_remove) > 0:
          idx = list(
              set(range(self.num_repr_points)) - set(idx_to_remove)
          )
          self.repr_points = self.repr_points[idx, :]
          self.repr_points_log = self.repr_points_log[idx]
  
      # We predict with the noise as we need to make sure that var is indeed positive definite.
      mu, _ = self.model.predict(self.repr_points)
      # we need a vector
      mu = np.ndarray.flatten(mu)
      var = self.model.predict_covariance(self.repr_points)
  
      (
          self.logP,
          self.dlogPdMu,
          self.dlogPdSigma,
          self.dlogPdMudMu,
      ) = epmgp.joint_min(mu, var, with_derivatives=True)
      # add a second dimension to the array
      self.logP = np.reshape(self.logP, (self.logP.shape[0], 1))
  
file_path: GPyOpt/GPyOpt/acquisitions/ES.py
imports: import scipy
  import numpy
  from .base import AcquisitionBase
  from .EI import AcquisitionEI
  from ..util import epmgp
  from ..models.gpmodel import GPModel
label_desc: Use image node_id 2 for calling the AcquisitionEntropySearch obj's underlying member method code with example usage: obj._update_parameters() without return types