def test_train_twice(self):
input_dimension = 3
sample_size = 500
X = np.random.rand(sample_size, input_dimension)
Y = np.random.randint(0, high=2, size=sample_size)
base_label = 'test_train_'
model = LayeredNeuralNetwork(input_dimension=input_dimension)
for i in range(2):
model.fit(X, Y, base_label + str(i))
def test_get_weight_correct_dimension(self):
input_dimension = 4
sample_size = 500
X = np.random.rand(sample_size, input_dimension)
Y = np.random.randint(0, high=2, size=sample_size)
label = 'weight_check'
model = LayeredNeuralNetwork(input_dimension=input_dimension)
model.fit(X, Y, label)
weights = model.get_weights()
self.assertTrue(weights.flatten().shape[0], input_dimension)
def test_identity_learn_perfect(self):
input_dimension = 1
sample_size = 500
Y = np.random.randint(0, high=2, size=sample_size)
X = Y.reshape([-1, 1])
label = 'identity'
model = LayeredNeuralNetwork(input_dimension=input_dimension)
model.fit(X, Y, label)
score = model.score(X, Y, label=label)
self.assertEqual(1, score)
def test_train_xor_score_succeed_with_pre_requisite(self):
input_dimension = 2
model = LayeredNeuralNetwork(input_dimension=input_dimension)
and_score = binary.Binary.teach_and(model)
self.assertEqual(1, and_score)
or_score = binary.Binary.teach_or(model)
self.assertEqual(1, or_score)
xor_score = binary.Binary.teach_xor(model)
self.assertEqual(1, xor_score)
def test_new_node_name(self):
label = 'hi'
expected_new_nod_name = 'hi' + utilities.node_version_separator + '0'
model = LayeredNeuralNetwork(2)
new_node_name = model.get_new_node_name(label)
self.assertEqual(expected_new_nod_name, new_node_name)
model.labels = [label]
model.label_to_node_name[label] = expected_new_nod_name
expected_new_nod_name2 = 'hi' + utilities.node_version_separator + '1'
new_node_name2 = model.get_new_node_name(label)
self.assertEqual(expected_new_nod_name2, new_node_name2)
def test_train_and_score(self):
input_dimension = 9
model = LayeredNeuralNetwork(input_dimension=input_dimension)
score = binary.Binary.teach_and(model)
self.assertEqual(1, score)
def test_frequency_all(self):
input_dimension = 20
model = LayeredNeuralNetwork(input_dimension=input_dimension)
score = frequency.Frequency.teach_all_frequency(model)
self.assertEqual(1, score)
def test_train_xor_score_fail_without_pre_requisite(self):
input_dimension = 9
model = LayeredNeuralNetwork(input_dimension=input_dimension)
score = binary.Binary.teach_xor(model)
self.assertLess(score, 0.7)