code: def test_simple_pnn(self):
      dataset = datasets.load_iris()
      data = dataset.data
      target = dataset.target
  
      test_data_size = 10
      skfold = StratifiedKFold(n_splits=test_data_size)
      avarage_result = 0
  
      for train, test in skfold.split(data, target):
          x_train, x_test = data[train], data[test]
          y_train, y_test = target[train], target[test]
  
          nw = algorithms.PNN(verbose=False, std=0.1)
          nw.train(x_train, y_train)
          result = nw.predict(x_test)
          avarage_result += sum(y_test == result)
  
      self.assertEqual(avarage_result / test_data_size, 14.4)
      self.assertPickledNetwork(nw, x_test)
  
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 2 for calling the PNNTestCase obj's underlying member method code with example usage: obj.test_simple_pnn() without return types