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","''"]
code: def __init__(
      self,
      gamma_osc: float = 0.7,
      gamma_pan: float = 1.0,
      eta: float = 0.9,
      minimum_window: Optional[Union[List[float], float]] = 0.0,
  ) -> None:
      self.gamma_osc = gamma_osc
      self.gamma_pan = gamma_pan
      self.eta = eta
      self.minimum_window_value = minimum_window
  
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 1 to create a new SequentialDomainReductionTransformer object from inherited base classes: DomainTransformer with example: obj = SequentialDomainReductionTransformer(gamma_osc, gamma_pan, eta, minimum_window)