docstr: Iteration class to resample new configurations along side keeping the good ones
  in SuccessiveHalving.
  
  Parameters:
  -----------
          resampling_rate: float
                  fraction of configurations that are resampled at each stage
          min_samples_advance:int
                  number of samples that are guaranteed to proceed to the next
                  stage regardless of the fraction.
docstr_tok: ["Iteration","class","to","resample","new","configurations","along","side","keeping","the","good","ones","in","SuccessiveHalving",".","Parameters",":","--","--","--","--","--","-","resampling_rate",":","float","fraction","of","configurations","that","are","resampled","at","each","stage","min_samples_advance",":","int","number","of","samples","that","are","guaranteed","to","proceed","to","the","next","stage","regardless","of","the","fraction","."]
code: def __init__(
      self, *args, resampling_rate=0.5, min_samples_advance=1, **kwargs
  ):
      """
      Iteration class to resample new configurations along side keeping the good ones
      in SuccessiveHalving.
  
      Parameters:
      -----------
              resampling_rate: float
                      fraction of configurations that are resampled at each stage
              min_samples_advance:int
                      number of samples that are guaranteed to proceed to the next
                      stage regardless of the fraction.
  
      """
      self.resampling_rate = resampling_rate
      self.min_samples_advance = min_samples_advance
  
imports: import sys
  from hpbandster.iterations.base import BaseIteration
  import numpy
label_desc: Use image node_id 1 to create a new SuccessiveResampling object from inherited base classes: BaseIteration with example: obj = SuccessiveResampling()