class_docstr: Class for fitting Gaussian processes. 
class_docstr_tok: ["Class","for","fitting","Gaussian","processes","."]
docstr: Sets up a bunch of ancillary parameters. 
docstr_tok: ["Sets","up","a","bunch","of","ancillary","parameters","."]
code: def _set_up(self):
      """Sets up a bunch of ancillary parameters."""
      # Set place-holders for hp_bounds or hp_vals depending on whether we are using ml or
      # post_sampling/mean
      # -----------------------------------------------------------------------------
      self.cts_hp_bounds = (
          []
      )  # The bounds for each hyper parameter should be a
      # num_cts_hps x 2 array where the 1st/2nd columns are the
      # lowe/upper bounds.
      self.dscr_hp_vals = (
          []
      )  # A list of lists where each list contains a list of values
      # for each discrete hyper-parameter.
      self.param_order = []
      # Set up for common things - including mean and noise variance hyper-parameters
      # -----------------------------------------------------------------------------
      epsilon = 0.0001
      self.Y_var = (
          np.array(self.Y).std() ** 2 + epsilon
          if len(self.Y) > 0
          else epsilon
      )
      self._set_up_mean_and_noise_variance_bounds()
      # Set up hyper-parameters for the child.
      # -----------------------------------------------------------------------------
      self._child_set_up()
      self._hp_tune_method_set_up()
      self.cts_hp_bounds = np.array(self.cts_hp_bounds)
      # Some post child set up
      if "ml" in self.methods_to_use:
          # The number of hyper parameters
          self.num_hps = len(self.cts_hp_bounds) + len(
              self.dscr_hp_vals
          )
          self._set_up_ml_hp_tune()
      if "post_sampling" in self.methods_to_use:
          self._set_up_post_sampling_hp_tune()
          self.num_hps = len(
              self.hp_priors
          )  # The number of hyper parameters
      if "post_mean" in self.methods_to_use:
          self._set_up_post_mean_hp_tune()
          self.num_hps = len(self.hp_priors)
  
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 2 for calling the GPFitter obj's underlying member method code with example usage: obj._set_up() without return types