def test_predict():
input_dimensions = 2
number_of_nodes = 2
model = SingleLayerNN(input_dimensions=input_dimensions, number_of_nodes=number_of_nodes)
model.initialize_weights(seed=1)
X_train = np.array([[-1.43815556, 0.10089809, -1.25432937, 1.48410426],
[-1.81784194, 0.42935033, -1.2806198, 0.06527391]])
Y_hat = model.predict(X_train)
assert np.array_equal(Y_hat, np.array([[1, 1, 1, 1], [1, 0, 1, 1]]))
def test_weight_initialization():
input_dimensions = 2
number_of_nodes = 5
model = SingleLayerNN(input_dimensions=2, number_of_nodes=number_of_nodes)
model.initialize_weights(seed=1)
assert model.weights.ndim == 2 and model.weights.shape[0] == number_of_nodes and model.weights.shape[
1] == input_dimensions + 1
weights = np.array([[1.62434536, -0.61175641, -0.52817175],
[-1.07296862, 0.86540763, -2.3015387],
[1.74481176, -0.7612069, 0.3190391],
[-0.24937038, 1.46210794, -2.06014071],
[-0.3224172, -0.38405435, 1.13376944]])
np.testing.assert_allclose(model.get_weights(), weights, rtol=1e-3, atol=1e-3)
def test_train_and_error_calculation():
input_dimensions = 6
number_of_nodes = 2
model = SingleLayerNN(input_dimensions=input_dimensions,
number_of_nodes=number_of_nodes)
X_train = np.random.randn(input_dimensions, 5)
X_train = np.array(
[[0.60302512, -0.94856749, -0.88904878, -0.02272178, 0.90226341],
[0.23682103, -0.29573089, -0.2328635, 0.85537468, 0.19151025],
[-0.35294026, -0.99263065, 0.41645806, -0.22561292, 2.72811021],
[0.06180665, 1.0643302, 0.49739215, -1.81960612, 0.50104263],
[-0.4875518, -0.98996947, 2.38729703, 0.95753127, -0.20929545],
[-1.20261055, -1.84727613, -1.13450254, -1.57499346, 0.4382195]])
Y_train = np.array([[0, 1, 1, 1, 0], [0, 0, 1, 0, 1]])
model.initialize_weights(seed=2)
error = []
for k in range(20):
error.append(model.calculate_percent_error(X_train, Y_train))
model.train(X_train, Y_train, num_epochs=1, alpha=0.025)
error.append(model.calculate_percent_error(X_train, Y_train))
np.testing.assert_allclose(error, [
80.0, 80.0, 80.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 40.0, 20.0,
20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 0.0, 0.0
],
rtol=1e-3,
atol=1e-3)
def test_set_and_get_weights():
input_dimensions = 4
number_of_nodes = 9
model = SingleLayerNN(input_dimensions=input_dimensions, number_of_nodes=number_of_nodes)
weights=model.get_weights()
assert weights.ndim == 2 and \
weights.shape[0] == number_of_nodes and \
weights.shape[1] == (input_dimensions + 1)
model.set_weights(np.ones((number_of_nodes, input_dimensions + 1)))
weights = model.get_weights()
assert weights.ndim == 2 and \
weights.shape[0] == number_of_nodes and \
weights.shape[1] == (input_dimensions + 1)
assert np.array_equal(model.get_weights(), np.ones((number_of_nodes, input_dimensions + 1)))
