def test_perceptron_init_errors(self):
with self.assertRaises(ValueError):
algorithms.Perceptron((2, 2, 1), verbose=False)
with self.assertRaises(ValueError):
algorithms.Perceptron((2, 2.5), verbose=False)
with self.assertRaises(NetworkConnectionError):
algorithms.Perceptron(layers.Input(2) > layers.Sigmoid(1),
verbose=False)
def test_predict_different_inputs(self):
pnet = algorithms.Perceptron((1, 1), verbose=False)
data = np.array([[1, 2, 3]]).T
target = np.array([[1, 1, 1]]).T
pnet.train(data, target)
self.assertInvalidVectorPred(pnet, data.ravel(), target, decimal=2)
def test_train(self):
input_data = np.array([[1, 0], [2, 2], [3, 3], [0, 0]])
target_data = np.array([[1], [0], [0], [1]])
prnet = algorithms.Perceptron((2, 1), step=0.1, verbose=False)
prnet.train(input_data, target_data, epochs=30)
predicted_result = prnet.predict(np.array([[4, 4], [0, 0]]))
self.assertEqual(prnet.errors.last(), 0)
self.assertEqual(predicted_result[0, 0], 0)
self.assertEqual(predicted_result[1, 0], 1)
def test_train_different_inputs(self):
self.assertInvalidVectorTrain(
algorithms.Perceptron((1, 1), verbose=False), np.array([1, 2, 3]),
np.array([1, 2, 3]))
def test_valid_cases(self):
algorithms.Perceptron(layers.Input(2) > layers.Step(1), verbose=False)
def test_perceptron_init_errors(self):
with self.assertRaises(ValueError):
algorithms.Perceptron((2, 2, 1), verbose=False)