class_docstr: Tuner with droplet algorithm.
  
  Parameters
  ----------
  start_position: list of int
      position initial of the space, the default is [0, 0, ..., 0]
  pvalue: float
      statistical value to confidence level, the default is 0.05
class_docstr_tok: ["Tuner","with","droplet","algorithm",".","Parameters","--","--","--","--","--","start_position",":","list","of","int","position","initial","of","the","space",",","the","default","is","[","0",",","0",",","...",",","0","]","pvalue",":","float","statistical","value","to","confidence","level",",","the","default","is","0.05"]
code: def __init__(self, task, start_position=None, pvalue=0.05):
      super(DropletTuner, self).__init__(task)
  
      # space info
      self.space = task.config_space
      self.dims = []
  
      for _, v in self.space.space_map.items():
          self.dims.append(len(v))
      if len(self.dims) == 0:
          self.dims.append(1)
  
      # start position
      start_position = (
          [0] * len(self.dims)
          if start_position is None
          else start_position
      )
      self.best_choice = (-1, [0] * len(self.dims), [99999])
      self.visited = set([self.space.knob2point(start_position)])
      self.execution, self.total_execution, self.pvalue = (
          1,
          max(self.dims),
          pvalue,
      )
      self.step, self.iter, self.batch = 1, 0, max(16, os.cpu_count())
      self.next = [
          (self.space.knob2point(start_position), start_position)
      ]
  
file_path: tvm/python/tvm/autotvm/tuner/droplet_tuner.py
imports: import logging
  import os
  import numpy
  from scipy import stats
  from .tuner import Tuner
label_desc: Use image node_id 1 to create a new DropletTuner object from inherited base classes: Tuner with example: obj = DropletTuner(task, start_position, pvalue)