class XorFunctionIntegrationTest(unittest.TestCase):
def setUp(self):
self.networkFile = 'network_XOR_SIG.xml'
self.network = NetParser(COMMON_DIR_PREFIX + self.networkFile).parse()
def tearDown(self):
pass
def testWithSigmoidActivationFunction(self):
input_vector = InputVectorParser(COMMON_DIR_PREFIX + 'input_0_0.xml').parse()
value = self.network.calculte_answer(input_vector)
print('f(0,0) = ', str(self.network.calculte_answer(input_vector)))
self.assertAlmostEqual(0, value[0], 1)
input_vector = InputVectorParser(COMMON_DIR_PREFIX + 'input_1_0.xml').parse()
value = self.network.calculte_answer(input_vector)
print('f(1,0) = ', str(self.network.calculte_answer(input_vector)))
self.assertAlmostEqual(1, value[0], 1)
input_vector = InputVectorParser(COMMON_DIR_PREFIX + 'input_0_1.xml').parse()
value = self.network.calculte_answer(input_vector)
print('f(0,1) = ', str(self.network.calculte_answer(input_vector)))
self.assertAlmostEqual(1, value[0], 1)
input_vector = InputVectorParser(COMMON_DIR_PREFIX + 'input_1_1.xml').parse()
value = self.network.calculte_answer(input_vector)
print('f(1,1) = ', str(self.network.calculte_answer(input_vector)))
self.assertAlmostEqual(0, value[0], 1)
def testWithThresholdActivationFunction(self):
pass
def test_backpropagation(self):
input_vectors = [InputVectorParser(COMMON_DIR_PREFIX + LEARNING_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 5)]
for input_vec in input_vectors:
print(input_vec)
#testVectors = [InputVectorParser(COMMON_DIR_PREFIX + TEST_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 4)]
network = NetParser(COMMON_DIR_PREFIX + NETWORK_FILE).parse()#lambda : random.uniform(-1.0, 1.0))
learning_rate = 0.7
momentum = 0.1
iterations = 2000
network.backpropagation_learn(input_vectors, learning_rate, iterations, momentum)
print(network)
error = 0.0
for inputVector in input_vectors :
inputVector.printVector(3)
outputDict = []
answer = network.calculte_answer(inputVector, False, outputDict)
print(answer)
for id, val in outputDict:
error = error + 0.5*(inputVector.expected_value_dict[id]-val)**2
print(error)
print(network.calculte_answer(InputVector({'input1' : 0.5, 'input2' : 0.5})))
weight_function = None
if args.random:
print(
"Weight of links between nodes will be set to random values from range [{}; {}]".format(
args.random[0], args.random[1]
)
)
weight_function = lambda: random.uniform(args.random[0], args.random[1])
elif args.zeros:
print("Weight of links between nodes will be set to 0.")
weight_function = lambda: 0.0
network = NetParser(args.network).parse(weight_function)
if args.vector_limits:
input_vector = RandomInputVectorFactory.create_new(
args.vector_limits[0], args.vector_limits[1], network.layers[0].get_nodes_ids()
)
print(
"Generated vector with values from range [{}; {}]:\n{}".format(
args.vector_limits[0], args.vector_limits[1], input_vector
)
)
else:
input_vectors = [InputVectorParser(vector).parse() for vector in args.vector_file]
if args.kohonen:
network.learning_process(input_vectors, args.coefficient, args.coefficient_half_life, args.turns)
if args.backpropagation:
network.backpropagation_learn(input_vectors, args.coefficient, args.turns)
else:
print("Network response is:")
print(network.calculte_answer(input_vectors[0]))
