code: def next(
      self, grid, values, durations, candidates, pending, complete
  ):
      # Don't bother using fancy GP stuff at first.
      if complete.shape[0] < 2:
          return int(candidates[0])
  
      # Perform the real initialization.
      if self.D == -1:
          self._real_init(grid.shape[1], values[complete])
  
      # Grab out the relevant sets.
      comp = grid[complete, :]
      cand = grid[candidates, :]
      pend = grid[pending, :]
      vals = values[complete]
  
      if self.mcmc_iters > 0:
          # Sample from hyperparameters.
  
          overall_ei = np.zeros((cand.shape[0], self.mcmc_iters))
  
          for mcmc_iter in range(self.mcmc_iters):
              self.sample_hypers(comp, vals)
              log(
                  "mean: %f  amp: %f  noise: %f  min_ls: %f  max_ls: %f"
                  % (
                      self.mean,
                      np.sqrt(self.amp2),
                      self.noise,
                      np.min(self.ls),
                      np.max(self.ls),
                  )
              )
  
              overall_ei[:, mcmc_iter] = self.compute_ei(
                  comp, pend, cand, vals
              )
  
          best_cand = np.argmax(np.mean(overall_ei, axis=1))
  
          return int(candidates[best_cand])
  
      else:
          # Optimize hyperparameters
          try:
              self.optimize_hypers(comp, vals)
          except:
              # Initial length scales.
              self.ls = np.ones(self.D)
              # Initial amplitude.
              self.amp2 = np.std(vals)
              # Initial observation noise.
              self.noise = 1e-3
          log(
              "mean: %f  amp: %f  noise: %f  min_ls: %f  max_ls: %f"
              % (
                  self.mean,
                  np.sqrt(self.amp2),
                  self.noise,
                  np.min(self.ls),
                  np.max(self.ls),
              )
          )
  
          ei = self.compute_ei(comp, pend, cand, vals)
  
          best_cand = np.argmax(ei)
  
          return int(candidates[best_cand])
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import numpy
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import pickle
  from ..Locker import *
  from ..helpers import *
  import logging
label_desc: Use image node_id 5 for calling the GPEIChooser obj's underlying member method code with example usage: obj.next(grid, values, durations, candidates, pending, complete) and returns: int, int, int