def test_prob_1d(self):
mlp = MultilayerPerceptron(
num_inputs=4, num_hidden_layers=1, num_hidden_nodes=3)
mlp.fit(X_TRAIN, LABELS_TRAIN, epochnum=5)
pred_prob = mlp.predict_prob(X_TRAIN)
assert np.all(np.logical_and(0 <= pred_prob, pred_prob <= 1))
def test_prob_1d(self):
mlp = MultilayerPerceptron(num_inputs=4,
num_hidden_layers=1,
num_hidden_nodes=3)
mlp.fit(X_TRAIN, LABELS_TRAIN, epochnum=5)
pred_prob = mlp.predict_prob(X_TRAIN)
assert np.all(np.logical_and(0 <= pred_prob, pred_prob <= 1))
def test_prob_2d(self):
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0, 1], [1, 0], [0, 1], [1, 0]])
mlp = MultilayerPerceptron(
num_inputs=3, num_outputs=2,
num_hidden_layers=1)
mlp.fit(x, y, epochnum=5)
pred_prob = mlp.predict_prob(x)
assert np.all(np.logical_and(0 <= pred_prob, pred_prob <= 1))
assert np.allclose(np.sum(pred_prob, axis=1), 1)
def test_prob_2d(self):
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0, 1], [1, 0], [0, 1], [1, 0]])
mlp = MultilayerPerceptron(num_inputs=3,
num_outputs=2,
num_hidden_layers=1)
mlp.fit(x, y, epochnum=5)
pred_prob = mlp.predict_prob(x)
assert np.all(np.logical_and(0 <= pred_prob, pred_prob <= 1))
assert np.allclose(np.sum(pred_prob, axis=1), 1)
