def test_activate_network_bad_input():
"""MarkovNetwork.activate() with bad input"""
np.random.seed(32480)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.states[0:2] = np.array([-7, 2])
test_mn.activate_network()
assert np.all(test_mn.states[-2:] == np.array([1, 0]))
def test_activate_network_bad_input():
"""MarkovNetwork.activate() with bad input"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.states[0:2] = np.array([-7, 2])
test_mn.activate_network()
assert np.all(test_mn.states[-2:] == np.array([0, 1]))
def test_activate_network():
"""MarkovNetwork.activate()"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.states[0:2] = np.array([1, 1])
test_mn.activate_network()
assert np.all(test_mn.states[-2:] == np.array([0, 1]))
def test_activate_network():
"""MarkovNetwork.activate()"""
np.random.seed(32480)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.states[0:2] = np.array([1, 1])
test_mn.activate_network()
assert np.all(test_mn.states[-2:] == np.array([1, 0]))
def test_update_input_states_invalid_input():
"""MarkovNetwork.test_update_input_states() with invalid input"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
try:
test_mn.update_input_states([1, 1, 0])
except Exception as e:
assert type(e) is ValueError
def test_update_input_states_invalid_input():
"""MarkovNetwork.test_update_input_states() with invalid input"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
try:
test_mn.update_input_states([1, 1, 0])
except Exception as e:
assert type(e) is ValueError
def test_get_output_states_bad_input():
"""MarkovNetwork.get_output_states() with bad input"""
np.random.seed(32480)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.update_input_states([-7, 2])
test_mn.activate_network()
assert np.all(test_mn.get_output_states() == np.array([1, 0]))
def test_get_output_states_bad_input():
"""MarkovNetwork.get_output_states() with bad input"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.update_input_states([-7, 2])
test_mn.activate_network()
assert np.all(test_mn.get_output_states() == np.array([0, 1]))
def test_get_output_states():
"""MarkovNetwork.get_output_states()"""
np.random.seed(32480)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.update_input_states([1, 1])
test_mn.activate_network()
assert np.all(test_mn.get_output_states() == np.array([1, 0]))
def test_init_seed_genome():
"""MarkovNetwork initializer with seeded genome"""
np.random.seed(4303423)
seed_genome = np.random.randint(0, 256, 10000)
seed_genome[0:2] = np.array([42, 213])
test_mn = MarkovNetwork(num_input_states=4,
num_memory_states=5,
num_output_states=6,
probabilistic=False,
genome=seed_genome)
assert np.all(test_mn.genome == seed_genome)
assert len(test_mn.markov_gates) == 1
def test_init():
"""MarkovNetwork initializer"""
np.random.seed(3938472)
test_mn = MarkovNetwork(num_input_states=4,
num_memory_states=5,
num_output_states=6,
seed_num_markov_gates=2,
probabilistic=False,
genome=None)
assert test_mn.num_input_states == 4
assert test_mn.num_memory_states == 5
assert test_mn.num_output_states == 6
assert len(test_mn.states) == 4 + 5 + 6
assert len(test_mn.markov_gates) == 2
assert np.max([len(x) for x in test_mn.markov_gate_input_ids
]) <= MarkovNetwork.max_markov_gate_inputs
assert np.max([len(x) for x in test_mn.markov_gate_output_ids
]) <= MarkovNetwork.max_markov_gate_outputs
def test_update_input_states():
"""MarkovNetwork.test_update_input_states()"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.update_input_states([1, 1])
assert np.all(test_mn.states[:2] == np.array([1, 1]))
def test_update_input_states_bad_input():
"""MarkovNetwork.test_update_input_states() with bad input"""
np.random.seed(98342)
test_mn = MarkovNetwork(2, 4, 2)
test_mn.update_input_states([-7, 2])
assert np.all(test_mn.states[:2] == np.array([1, 1]))
