def test_majority_correct_return_types(neg_feedback):
"""Check that the function returns what we expect"""
N, J, J_pseudo = neg_feedback
maxT = 100
convergence, s, H, UH, ic = majority(N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT)
assert isinstance(convergence, type(True))
assert isinstance(s, np.ndarray)
assert isinstance(H, list)
assert isinstance(UH, list)
assert isinstance(ic, np.ndarray)
def test_majority_steady_states_are_steady(random_small):
"""Check that steady states are really steady"""
N, J, J_pseudo = random_small
maxT = 10000
for _ in range(1000):
convergence, s, _, _, _ = majority(
N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
)
if convergence:
assert np.all(np.sign(J_pseudo @ s) == s)
def test_majority_energies(random_small):
"""Check that H and UH are consistent"""
N, J, J_pseudo = random_small
maxT = 10000
for _ in range(1000):
_, _, H, UH, _ = majority(
N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
)
assert len(UH) <= len(H)
for e in UH:
assert e in H
def test_majority_correct_autogenerated_initial_conditions(random_small):
"""
Check that when no initial condition is given, the one
autogenerated is a valid one, i.e. [-1, 1]^N
"""
N, J, J_pseudo = random_small
maxT = 100
for _ in range(1000):
_, _, _, _, ic = majority(
N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
)
assert isinstance(ic, np.ndarray)
assert tuple(ic) in product([-1., 1.], repeat=N)
def test_majority_block_one_node(random_small):
"""Check that blocked nodes are not updated"""
N, J, J_pseudo = random_small
maxT = 10000
for _ in range(100):
initial_condition = np.random.randint(2, size=N) * 2. - 1.
# block only one node
for i in range(N):
can_be_updated = np.concatenate(
[np.arange(0, i), np.arange(i + 1, N)])
_, s, _, _, _ = majority(N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
initial_condition=initial_condition,
can_be_updated=can_be_updated)
assert s[i] == initial_condition[i]
def test_majority_correct_return_types_with_can_be_updated(neg_feedback):
"""
Check that the function returns what we expect
when we pass the set of nodes that can be updated
"""
N, J, J_pseudo = neg_feedback
maxT = 100
can_be_updated = np.array([0])
convergence, s, H, UH, ic = majority(N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
can_be_updated=can_be_updated)
assert isinstance(convergence, type(True))
assert isinstance(s, np.ndarray)
assert isinstance(H, list)
assert isinstance(UH, list)
assert isinstance(ic, np.ndarray)
def test_majority_correct_return_types_with_initial_conditions(neg_feedback):
"""
Check that the function returns what we expect
when we pass the initial conditions
"""
N, J, J_pseudo = neg_feedback
maxT = 100
initial_condition = np.array([1., 1.])
convergence, s, H, UH, ic = majority(N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
initial_condition=initial_condition)
assert isinstance(convergence, type(True))
assert isinstance(s, np.ndarray)
assert isinstance(H, list)
assert isinstance(UH, list)
assert isinstance(ic, np.ndarray)
def test_majority_block_many_node(random_small):
"""Check that blocked nodes are not updated"""
N, J, J_pseudo = random_small
maxT = 10000
for _ in range(1000):
initial_condition = np.random.randint(2, size=N) * 2. - 1.
# block some nodes at random
can_be_updated = np.random.choice(range(N),
size=int(N / 2),
replace=False)
blocked = [x for x in range(N) if x not in can_be_updated]
_, s, _, _, _ = majority(N=N,
J=J,
J_pseudo=J_pseudo,
maxT=maxT,
initial_condition=initial_condition,
can_be_updated=can_be_updated)
for i in blocked:
assert s[i] == initial_condition[i]