class_docstr: Class for Local Penalization acquisition. Used for batch design.
  :param model: model of the class GPyOpt
  :param space: design space of the class GPyOpt.
  :param optimizer: optimizer of the class GPyOpt.
  :param acquisition: acquisition function of the class GPyOpt
  :param transform: transformation applied to the acquisition (default, none).
  
  .. Note:: irrespective of the transformation applied the penalized acquisition is always mapped again to the log space.
  This way gradients can be computed additively and are more stable.
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docstr: Computes the value of the penalizer (centered at x_0) at any x.
docstr_tok: ["Computes","the","value","of","the","penalizer","(","centered","at","x_0",")","at","any","x","."]
code: def _d_hammer_function(self, x, X_batch, r_x0, s_x0):
      """
      Computes the value of the penalizer (centered at x_0) at any x.
      """
      dx = (
          np.atleast_2d(x)[:, None, :]
          - np.atleast_2d(X_batch)[None, :, :]
      )
      nm = np.sqrt((np.square(dx)).sum(-1))
      z = (nm - r_x0) / s_x0
      h_func = norm.cdf(z)
  
      d = (
          1.0
          / (s_x0 * np.sqrt(2 * np.pi) * h_func)
          * np.exp(-np.square(z) / 2)
          / nm
      )
      d[h_func < 1e-50] = 0.0
      d = d[:, :, None]
      return d.sum(axis=1)
  
file_path: GPyOpt/GPyOpt/acquisitions/LP.py
imports: from .base import AcquisitionBase
  from .LCB import AcquisitionLCB
  from .LCB_mcmc import AcquisitionLCB_MCMC
  import numpy
  from scipy.stats import norm
label_desc: Use image node_id 6 for calling the AcquisitionLP obj's underlying member method code with example usage: obj._d_hammer_function(x, X_batch, r_x0, s_x0) and returns: d