class_docstr: Contains unit tests for the TransportNNDistanceComputer class. 
class_docstr_tok: ["Contains","unit","tests","for","the","TransportNNDistanceComputer","class","."]
docstr: Testing hallucinated predictions for NNGP. 
docstr_tok: ["Testing","hallucinated","predictions","for","NNGP","."]
code: def test_hallucinated_predictions(self):
      """Testing hallucinated predictions for NNGP."""
      self.report("Testing hallucinated predictions for NNGP.")
      num_coeffs_vals = [2, 1, 4, 5] * 5
      for dataset in self.datasets:
          for dist_type in self.dist_types:
              for kernel_type in self.kernel_types[dist_type]:
                  curr_num_coeffs = num_coeffs_vals.pop(0)
                  curr_gp = build_nngp_with_dataset(
                      dataset, kernel_type, curr_num_coeffs, dist_type
                  )
                  curr_preds, curr_stds = curr_gp.eval(
                      dataset[2][1:], "std"
                  )
                  (
                      ha_preds,
                      ha_stds,
                  ) = curr_gp.eval_with_hallucinated_observations(
                      dataset[2][1:],
                      dataset[0][4:] + dataset[2][:1],
                      "std",
                  )
                  assert np.linalg.norm(curr_preds - ha_preds) < _TOL
                  assert np.all(curr_stds >= ha_stds)
  
imports: import numpy
  import unittest
  from .syn_nn_functions import syn_func1_common, cnn_syn_func1, mlp_syn_func1
  from .nn_examples import generate_cnn_architectures, generate_mlp_architectures
  from .None import nn_gp
  from ..utils.ancillary_utils import get_list_of_floats_as_str
  from ..utils.base_test_class import BaseTestClass, execute_tests
  from ..utils.option_handler import load_options
label_desc: Use image node_id 3 for calling the NNGPTestCase obj's underlying member method code with example usage: obj.test_hallucinated_predictions() without return types