def test_logistics():
trajectories = MinimalFsPeptide().get_cached().trajectories
logisticcontactfeaturizer = LogisticContactFeaturizer()
logistics = logisticcontactfeaturizer.transform(trajectories)
assert logistics[0].shape[1] == 171
assert np.amax(logistics[0]) < 1.0
assert np.amin(logistics[0]) > 0.0
def test_logistics():
trajectories = MinimalFsPeptide().get_cached().trajectories
logisticcontactfeaturizer = LogisticContactFeaturizer()
logistics = logisticcontactfeaturizer.transform(trajectories)
assert logistics[0].shape[1] == 171
assert np.amax(logistics[0]) < 1.0
assert np.amin(logistics[0]) > 0.0
def test_binary_to_logistics():
trajectories = MinimalFsPeptide().get_cached().trajectories
steepness = np.absolute(10 * np.random.randn())
center = np.absolute(np.random.randn())
binarycontactfeaturizer = BinaryContactFeaturizer(cutoff=center)
binaries = binarycontactfeaturizer.transform(trajectories)
logisticcontactfeaturizer = LogisticContactFeaturizer(center=center, steepness=steepness)
logistics = logisticcontactfeaturizer.transform(trajectories)
# This checks that no distances that are larger than the center are logistically
# transformed such that they are less than 1/2
np.testing.assert_array_almost_equal(binaries[0], logistics[0] > 0.5)
def test_binary_to_logistics():
trajectories = MinimalFsPeptide().get_cached().trajectories
steepness = np.absolute(10 * np.random.randn())
center = np.absolute(np.random.randn())
binarycontactfeaturizer = BinaryContactFeaturizer(cutoff=center)
binaries = binarycontactfeaturizer.transform(trajectories)
logisticcontactfeaturizer = LogisticContactFeaturizer(center=center,
steepness=steepness)
logistics = logisticcontactfeaturizer.transform(trajectories)
# This checks that no distances that are larger than the center are logistically
# transformed such that they are less than 1/2
np.testing.assert_array_almost_equal(binaries[0], logistics[0] > 0.5)
def test_distance_to_logistic():
trajectories = MinimalFsPeptide().get_cached().trajectories
steepness = np.absolute(10 * np.random.randn())
center = np.absolute(np.random.randn())
contactfeaturizer = ContactFeaturizer()
contacts = contactfeaturizer.transform(trajectories)
logisticcontactfeaturizer = LogisticContactFeaturizer(center=center, steepness=steepness)
logistics = logisticcontactfeaturizer.transform(trajectories)
for n in range(10):
i = np.random.randint(0, contacts[0].shape[0] - 1)
j = np.random.randint(0, contacts[0].shape[1] - 1)
x = contacts[0][i][j]
y = logistics[0][i][j]
if x > center:
assert y < 0.5
if x < center:
assert y > 0.5
def test_distance_to_logistic():
trajectories = MinimalFsPeptide().get_cached().trajectories
steepness = np.absolute(10 * np.random.randn())
center = np.absolute(np.random.randn())
contactfeaturizer = ContactFeaturizer()
contacts = contactfeaturizer.transform(trajectories)
logisticcontactfeaturizer = LogisticContactFeaturizer(center=center,
steepness=steepness)
logistics = logisticcontactfeaturizer.transform(trajectories)
for n in range(10):
i = np.random.randint(0, contacts[0].shape[0] - 1)
j = np.random.randint(0, contacts[0].shape[1] - 1)
x = contacts[0][i][j]
y = logistics[0][i][j]
if (x > center):
assert y < 0.5
if (x < center):
assert y > 0.5
