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]
      numcand = cand.shape[0]
  
      # Spray a set of candidates around the min so far
      best_comp = np.argmin(vals)
      cand2 = np.vstack(
          (
              np.random.randn(10, comp.shape[1]) * 0.001
              + comp[best_comp, :],
              cand,
          )
      )
  
      if self.mcmc_iters > 0:
          # Possibly burn in.
          if self.needs_burnin:
              for mcmc_iter in range(self.burnin):
                  self.sample_hypers(comp, vals)
                  log(
                      "BURN %d/%d] mean: %.2f  amp: %.2f "
                      "noise: %.4f  min_ls: %.4f  max_ls: %.4f"
                      % (
                          mcmc_iter + 1,
                          self.burnin,
                          self.mean,
                          np.sqrt(self.amp2),
                          self.noise,
                          np.min(self.ls),
                          np.max(self.ls),
                      )
                  )
              self.needs_burnin = False
  
          # Sample from hyperparameters.
          # Adjust the candidates to hit ei peaks
          self.hyper_samples = []
          for mcmc_iter in range(self.mcmc_iters):
              self.sample_hypers(comp, vals)
              log(
                  "%d/%d] mean: %.2f  amp: %.2f  noise: %.4f "
                  "min_ls: %.4f  max_ls: %.4f"
                  % (
                      mcmc_iter + 1,
                      self.mcmc_iters,
                      self.mean,
                      np.sqrt(self.amp2),
                      self.noise,
                      np.min(self.ls),
                      np.max(self.ls),
                  )
              )
          self.dump_hypers()
  
          b = []  # optimization bounds
          for i in range(0, cand.shape[1]):
              b.append((0, 1))
  
          overall_ei = self.ei_over_hypers(comp, pend, cand2, vals)
          inds = np.argsort(np.mean(overall_ei, axis=1))[
              -self.grid_subset :
          ]
          cand2 = cand2[inds, :]
  
          # Optimize each point in parallel
          if self.use_multiprocessing:
              pool = multiprocessing.Pool(self.grid_subset)
              results = [
                  pool.apply_async(
                      optimize_pt,
                      args=(c, b, comp, pend, vals, copy.copy(self)),
                  )
                  for c in cand2
              ]
              for res in results:
                  cand = np.vstack((cand, res.get(1e8)))
              pool.close()
          else:
              # This is old code to optimize each point in parallel.
              for i in range(0, cand2.shape[0]):
                  log(
                      "Optimizing candidate %d/%d"
                      % (i + 1, cand2.shape[0])
                  )
                  # self.check_grad_ei(cand2[i,:].flatten(), comp, pend, vals)
                  ret = spo.fmin_l_bfgs_b(
                      self.grad_optimize_ei_over_hypers,
                      cand2[i, :].flatten(),
                      args=(comp, pend, vals),
                      bounds=b,
                      disp=0,
                  )
                  cand2[i, :] = ret[0]
              cand = np.vstack((cand, cand2))
  
          overall_ei = self.ei_over_hypers(comp, pend, cand, vals)
          best_cand = np.argmax(np.mean(overall_ei, axis=1))
  
          if best_cand >= numcand:
              return (int(numcand), cand[best_cand, :])
  
          return int(candidates[best_cand])
  
      else:
          # Optimize hyperparameters
          self.optimize_hypers(comp, vals)
  
          log(
              "mean: %.2f  amp: %.2f  noise: %.4f  "
              "min_ls: %.4f  max_ls: %.4f"
              % (
                  self.mean,
                  np.sqrt(self.amp2),
                  self.noise,
                  np.min(self.ls),
                  np.max(self.ls),
              )
          )
  
          # Optimize over EI
          b = []  # optimization bounds
          for i in range(0, cand.shape[1]):
              b.append((0, 1))
  
          for i in range(0, cand2.shape[0]):
              ret = spo.fmin_l_bfgs_b(
                  self.grad_optimize_ei,
                  cand2[i, :].flatten(),
                  args=(comp, vals, True),
                  bounds=b,
                  disp=0,
              )
              cand2[i, :] = ret[0]
          cand = np.vstack((cand, cand2))
  
          ei = self.compute_ei(comp, pend, cand, vals)
          best_cand = np.argmax(ei)
  
          if best_cand >= numcand:
              return (int(numcand), cand[best_cand, :])
  
          return int(candidates[best_cand])
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import pickle
  import copy
  import numpy
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import multiprocessing
  from ..helpers import *
  from ..Locker import *
  import logging
label_desc: Use image node_id 7 for calling the GPEIOptChooser obj's underlying member method code with example usage: obj.next(grid, values, durations, candidates, pending, complete) and returns: int, int, int, int,  cand, int,  cand