class_docstr: Class for fitting Gaussian processes. 
class_docstr_tok: ["Class","for","fitting","Gaussian","processes","."]
docstr: Sets up probabilities for hp tune methods. 
docstr_tok: ["Sets","up","probabilities","for","hp","tune","methods","."]
code: def _hp_tune_method_set_up(self):
      """Sets up probabilities for hp tune methods."""
      self.methods_to_use = [
          elem.lower()
          for elem in self.options.hp_tune_criterion.split("-")
      ]
      for method in self.methods_to_use:
          if method not in ["ml", "post_sampling", "post_mean"]:
              raise ValueError(
                  "hp_tune_criterion should be ml or post_sampling."
              )
      self.methods_to_use_counter = {
          key: 0 for key in self.methods_to_use
      }
      if self.options.hp_tune_probs == "uniform":
          self.hp_tune_probs = np.ones(
              len(self.methods_to_use)
          ) / float(len(self.methods_to_use))
      elif self.options.hp_tune_probs == "adaptive":
          self.hp_tune_uniform_sampling_prob = 0.05
          self.hp_tune_sampling_weights = {
              key: 1.0 for key in self.methods_to_use
          }
          self.hp_tune_probs = self._get_adaptive_hp_tune_probs()
      else:
          self.hp_tune_probs = np.array(
              [float(x) for x in self.options.hp_tune_probs.split("-")]
          )
          if len(self.hp_tune_probs) != len(self.methods_to_use):
              self.hp_tune_probs = np.ones(
                  len(self.methods_to_use)
              ) / float(len(self.methods_to_use))
      self.hp_tune_probs = self.hp_tune_probs / self.hp_tune_probs.sum()
  
imports: from __future__ import division
  from argparse import Namespace
  from itertools import product
  import sys
  import numpy
  from ..distributions.model import Model
  from ..distributions import continuous
  from ..distributions import discrete
  from ..utils.general_utils import stable_cholesky, draw_gaussian_samples, project_symmetric_to_psd_cone, solve_lower_triangular, solve_upper_triangular
  from ..utils.oper_utils import direct_ft_maximise, pdoo_maximise, random_maximise, random_sample_cts_dscr
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 3 for calling the GPFitter obj's underlying member method code with example usage: obj._hp_tune_method_set_up() without return types