text: import numpy as np
  
  # Load parameters
  params = np.loadtxt("input/parameters.txt", dtype=str, delimiter=':')
  
  # Random numbers seed
  seed = int(params[params[:, 0] == "seed", 1])
  rng = np.random.default_rng(seed)
  
  # Load training set
  training_set = np.loadtxt("input/training_set.csv", dtype=float, delimiter=',')
  
  # Number of features
  n_features = training_set.shape[1]
  
  # Number of presenters sets
  n_presenters_sets = int(params[params[:, 0] == "presenters sets", 1])
  
  # Number of presenters/detectors
  n_presenters = n_detectors = n_features * n_presenters_sets
  
  # Build detectors' global lists
  global_lists = np.arange(2*n_presenters, dtype=int)
  global_lists = np.tile(global_lists, reps=(n_detectors, 1))
  
  # Shuffle global lists
  global_lists = rng.permuted(global_lists, axis=1)
  
  # Export global lists
  np.savetxt("input/untrained_global_lists.csv", global_lists, fmt='%i', delimiter=',')
  