def assertInvalidVectorTrain(self,
network,
input_vector,
target=None,
decimal=5,
is_feature1d=True,
**train_kwargs):
"""
Method helps test network prediction training using different
types of row or column vector.
"""
input_vectors = vectors_for_testing(input_vector, is_feature1d)
if target is not None:
target_vectors = vectors_for_testing(target, is_feature1d)
input_vectors = zip(input_vectors, target_vectors)
train_args = inspect.getargspec(network.train).args
if 'epochs' in train_args and 'epochs' not in train_kwargs:
train_kwargs['epochs'] = 5
elif 'epsilon' in train_args and 'epsilon' not in train_kwargs:
train_kwargs['epsilon'] = 0.1
for i, input_data in enumerate(input_vectors, start=1):
if target is None:
network.train(input_data, **train_kwargs)
else:
network.train(*input_data, **train_kwargs)
def assertInvalidVectorTrain(self, network, input_vector, target=None,
decimal=5, is_feature1d=True, **train_kwargs):
"""
Method helps test network prediction training using different
types of row or column vector.
"""
input_vectors = vectors_for_testing(input_vector, is_feature1d)
if target is not None:
target_vectors = vectors_for_testing(target, is_feature1d)
input_vectors = zip(input_vectors, target_vectors)
train_args = inspect.getargspec(network.train).args
if 'epochs' in train_args and 'epochs' not in train_kwargs:
train_kwargs['epochs'] = 5
elif 'epsilon' in train_args and 'epsilon' not in train_kwargs:
train_kwargs['epsilon'] = 0.1
for i, input_data in enumerate(input_vectors, start=1):
if target is None:
network.train(input_data, **train_kwargs)
else:
network.train(*input_data, **train_kwargs)
def assertInvalidVectorPred(self, network, input_vector, target,
decimal=5, is_feature1d=True):
""" Method helps test network prediction procedure using different
types of row or column vector.
"""
test_vectors = vectors_for_testing(input_vector, is_feature1d)
for i, test_vector in enumerate(test_vectors, start=1):
predicted_vector = network.predict(test_vector)
np.testing.assert_array_almost_equal(predicted_vector, target,
decimal=decimal)
def test_bam_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 = vectors_for_testing(data.reshape(data.size),
is_feature1d=False)
for test_vector in test_vectors:
np.testing.assert_array_almost_equal(
bamnet.predict(test_vector)[1], target)
def test_bam_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 = vectors_for_testing(data.reshape(data.size),
is_feature1d=False)
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=0.01, epochs=100)
test_vectors = vectors_for_testing(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 assertInvalidVectorPred(self,
network,
input_vector,
target,
decimal=5,
is_feature1d=True):
""" Method helps test network prediction procedure using different
types of row or column vector.
"""
test_vectors = vectors_for_testing(input_vector, is_feature1d)
for i, test_vector in enumerate(test_vectors, start=1):
predicted_vector = network.predict(test_vector)
np.testing.assert_array_almost_equal(predicted_vector,
target,
decimal=decimal)
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=0.01, epochs=100)
test_vectors = vectors_for_testing(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
)
