code: def test_pnn_mini_batches(self):
      dataset = datasets.load_digits()
      n_classes = len(np.unique(dataset.target))
  
      x_train, x_test, y_train, y_test = train_test_split(
          dataset.data, dataset.target, test_size=0.3
      )
  
      pnnet = algorithms.PNN(verbose=False, batch_size=100, std=10)
      pnnet.train(x_train, y_train)
  
      y_predicted = pnnet.predict(x_test)
      self.assertEqual(y_predicted.shape, y_test.shape)
  
      y_predicted = pnnet.predict_proba(x_test)
      self.assertEqual(y_predicted.shape, (y_test.shape[0], n_classes))
  
file_path: neupy/tests/algorithms/rbfn/test_pnn.py
imports: from __future__ import division
  import numpy
  from sklearn import datasets
  from sklearn import metrics
  from sklearn.model_selection import StratifiedKFold, train_test_split
  from neupy import algorithms
  from neupy.exceptions import NotTrained
  from base import BaseTestCase
label_desc: Use image node_id 7 for calling the PNNTestCase obj's underlying member method code with example usage: obj.test_pnn_mini_batches() without return types