def test_predict_different_inputs(self):
bamnet = algorithms.DiscreteBAM()
data = np.array([[1, 0, 0, 1]])
target = np.array([[1, 0]])
bamnet.train(data, target)
test_vectors = create_vectors(data.reshape(data.size), row1d=True)
for test_vector in test_vectors:
np.testing.assert_array_almost_equal(
bamnet.predict(test_vector)[1], target)
def test_predict_different_inputs(self):
bamnet = algorithms.DiscreteBAM()
data = np.array([[1, 0, 0, 1]])
target = np.array([[1, 0]])
bamnet.train(data, target)
test_vectors = create_vectors(data.reshape(data.size), row1d=True)
for test_vector in test_vectors:
np.testing.assert_array_almost_equal(
bamnet.predict(test_vector)[1],
target
)
def test_reconstruct_different_inputs(self):
ojanet = algorithms.Oja(minimized_data_size=1,
verbose=False,
step=0.01)
data = np.array([[1, 2, 3]]).T
target = np.array([[1, 2, 3]]).T
input_vector = data.ravel()
ojanet.train(data, epsilon=0.1)
test_vectors = create_vectors(input_vector)
for i, test_vector in enumerate(test_vectors, start=1):
np.testing.assert_array_almost_equal(
ojanet.reconstruct(test_vector), target, decimal=1)
def test_reconstruct_different_inputs(self):
ojanet = algorithms.Oja(minimized_data_size=1, verbose=False,
step=0.01)
data = np.array([[1, 2, 3]]).T
target = np.array([[1, 2, 3]]).T
input_vector = data.ravel()
ojanet.train(data, epsilon=0.1)
test_vectors = create_vectors(input_vector)
for i, test_vector in enumerate(test_vectors, start=1):
np.testing.assert_array_almost_equal(
ojanet.reconstruct(test_vector),
target,
decimal=1
)
