old_contents: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Created on Sat Aug 26 00:24:39 2017
  
  Tests for neural_network.py
  
  @author: duc
  """
  
  import numpy as np
  import unittest
  import neural_network as nn
  
  class NeuralNetworkTests(unittest.TestCase):
  
      def test_xor(self):
          input = np.array([[0,0],[1,0],[0,1],[1,1]])
          output = np.array([[0.],[1.],[1.],[0.]])
          net = nn.NeuralNetwork(30000,1,2,1,1, input, output, [], [], [], [])
          net.train()
          decimal = 1
          np.testing.assert_array_almost_equal(net.forward_pass(input)[-1], output, decimal)
  
      def test_to_binary(self):
          m = np.array([[0.9, 0.5, 0.6],[0.003, 0.4, 0.59 ],[0.8, 0.5, 0.49]])
          binarised = np.array([[1, 0, 1],[0, 0, 1],[1, 0, 0]])
          np.array_equal(nn.get_binary(m), binarised)
  
  
  if __name__ == '__main__':
      unittest.main()
new_contents: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Created on Sat Aug 26 00:24:39 2017
  
  Tests for neural_network.py
  
  @author: duc
  """
  
  import numpy as np
  import unittest
  import neural_network as nn
  
  class NeuralNetworkTests(unittest.TestCase):
  
      def test_xor(self):
          input = np.array([[0,0],[1,0],[0,1],[1,1]])
          output = np.array([[0.],[1.],[1.],[0.]])
          net = nn.NeuralNetwork(6000,1,2,1,1, input, output, [], [], [], [])
          net.train()
          decimal = 1
          np.testing.assert_array_almost_equal(net.forward_pass(input)[-1], output, decimal)
  
      def test_to_binary(self):
          m = np.array([[0.9, 0.5, 0.6],[0.003, 0.4, 0.59 ],[0.8, 0.5, 0.49]])
          binarised = np.array([[1, 0, 1],[0, 0, 1],[1, 0, 0]])
          np.array_equal(nn.get_binary(m), binarised)
  
  
  if __name__ == '__main__':
      unittest.main()
subject: update unit test