code: def test_grrn_exceptions(self):
      with self.assertRaises(ValueError):
          # size of target data not the same as size of
          # input data.
          grnet = algorithms.GRNN(std=0.1, verbose=False)
          grnet.train(np.array([[0], [0]]), np.array([0]))
  
      with self.assertRaises(ValueError):
          # 2 features for target data
          grnet = algorithms.GRNN(std=0.1, verbose=False)
          grnet.train(np.array([[0], [0]]), np.array([[0, 0]]))
  
      with self.assertRaises(ValueError):
          # invalid feature size for prediction data
          grnet = algorithms.GRNN(std=0.1, verbose=False)
          grnet.train(np.array([[0], [0]]), np.array([0]))
          grnet.predict(np.array([[0]]))
  
      with self.assertRaises(NotTrained):
          # Prediction without training
          grnet = algorithms.GRNN(std=0.1, verbose=False)
          grnet.predict(np.array([[0]]))
  
      with self.assertRaises(ValueError):
          # different number of features for
          # train and test data
          grnet = algorithms.GRNN(std=0.1, verbose=False)
          grnet.train(np.array([[0]]), np.array([0]))
          grnet.predict(np.array([[0, 0]]))
  
file_path: neupy/tests/algorithms/rbfn/test_grnn.py
imports: import numpy
  from sklearn import datasets, metrics
  from sklearn.model_selection import train_test_split
  from neupy import algorithms
  from neupy.exceptions import NotTrained
  from base import BaseTestCase
label_desc: Use image node_id 1 for calling the GRNNTestCase obj's underlying member method code with example usage: obj.test_grrn_exceptions() without return types