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"]
docstr: returns the neighbors of the best solution
docstr_tok: ["returns","the","neighbors","of","the","best","solution"]
code: def next_pos(self, new_positions):
      "returns the neighbors of the best solution"
      next_set = []
      for p in new_positions:
          if len(next_set) > self.batch:
              break
          new_p = [
              (x + y) % self.dims[i] if (x + y > 0) else 0
              for i, (x, y) in enumerate(zip(p, self.best_choice[1]))
          ]
          idx_p = self.space.knob2point(new_p)
          if idx_p not in self.visited:
              self.visited.add(idx_p)
              next_set.append((idx_p, new_p))
      return next_set
  
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 4 for calling the DropletTuner obj's underlying member method code with example usage: obj.next_pos(new_positions) and returns: next_set