class_docstr: A sequential domain reduction transformer based on the work by Stander, N. and Craig, K:
  "On the robustness of a simple domain reduction scheme for simulation‐based optimization"
class_docstr_tok: ["A","sequential","domain","reduction","transformer","based","on","the","work","by","Stander",",","N.","and","Craig",",","K",":","''","On","the","robustness","of","a","simple","domain","reduction","scheme","for","simulation\u2010based","optimization","''"]
docstr: Reduces the bounds of the target space.
          
docstr_tok: ["Reduces","the","bounds","of","the","target","space","."]
code: def transform(self, target_space: TargetSpace) -> dict:
      """Reduces the bounds of the target space."""
      self._update(target_space)
  
      new_bounds = np.array(
          [
              self.current_optimal - 0.5 * self.r,
              self.current_optimal + 0.5 * self.r,
          ]
      ).T
  
      new_bounds = self._trim(new_bounds, self.original_bounds)
      self.bounds.append(new_bounds)
      return self._create_bounds(target_space.keys, new_bounds)
  
file_path: BayesianOptimization/bayes_opt/domain_reduction.py
imports: from typing import Optional, Union, List
  import numpy
  from .target_space import TargetSpace
  from warnings import warn
label_desc: Use image node_id 7 for calling the SequentialDomainReductionTransformer obj's underlying member method code with example usage: obj.transform(target_space) and returns: self